1 to 25 of 280 AWS Lambda Jobs in the UK excluding London

We have one open position of .NET Developer with AWS(Serverless) with one of our client based at Leeds, UK. This is a Permanent/Fulltime position. Below is the job description for your reference. Please share your application once you are interested. Role: .NET Developer with AWS(DevOps & Serverless) Location: Leeds, UK Mode: Permanent/Fulltime Hybrid … Job Description: Position Overview We are seeking an experienced AWS .NET Core Developer with strong expertise in building modern, cloud-native applications. The ideal candidate will have hands-on experience in serverless architectures , DevOps pipelines , and a strong Back End development background using .NET Core . Knowledge of Front End frameworks such as React or Blazor is an added … advantage. Create Job description for Required Skills Strong experience in .NET Core/ASP.NET Core (Web APIs, microservices). Expertise in AWS serverless services (Lambda, API Gateway, DynamoDB, S3, etc.). Knowledge of DevOps practices and CI/CD pipelines. Proficiency with Git and automated deployment strategies. Experience with SQL/NoSQL databases (SQL Server, PostgreSQL, DynamoDB). More ❯

We at Qualient solutions hiring for .NET Developer with strong AWS experience. Job Description: We are seeking an experienced AWS .NET Core Developer with strong expertise in building modern, cloud-native applications. The ideal candidate will have hands-on experience in serverless architectures, DevOps pipelines, and a strong Back End development background using .NET Core. Knowledge of Front … End frameworks such as React or Blazor is an added advantage. Required Skills: Strong experience in .NET Core/ASP.NET Core (Web APIs, microservices). Expertise in AWS serverless services (Lambda, API Gateway, DynamoDB, S3, etc.). Knowledge of DevOps practices and CI/CD pipelines. Proficiency with Git and automated deployment strategies. Experience with SQL/NoSQL … work in an agile team. Strong verbal and written communication skills, with the ability to articulate technical concepts clearly to both technical and non-technical stakeholders Good to have AWS Certified Developer - Associate certification. Key Responsibilities: Design, develop, and maintain scalable serverless applications using AWS services such as: AWS Lambda, API Gateway, DynamoDB, S3, Step Functions More ❯

Role Title: AWS .Net Developer Role Location: Leeds, UK Role Type: Permanent (Hybrid) Job Description:- Required Skills Strong experience in .NET Core/ASP.NET Core (Web APIs, microservices). Expertise in AWS serverless services (Lambda, API Gateway, DynamoDB, S3, etc.). Knowledge of DevOps practices and CI/CD pipelines. Proficiency with Git and automated deployment strategies. … work in an agile team. Strong verbal and written communication skills, with the ability to articulate technical concepts clearly to both technical and non-technical stakeholders Good to have AWS Certified Developer – Associate certification Key Responsibilities Design, develop, and maintain scalable serverless applications using AWS services such as: AWS Lambda, API Gateway, DynamoDB, S3, Step Functions … Build and optimize .NET Core microservices and RESTful APIs. Implement and manage CI/CD pipelines using Azure DevOps, or GitHub Actions,. Apply Infrastructure as Code (IaC) using AWS CDK, Terraform, or CloudFormation. Ensure application security, monitoring, and logging using AWS CloudWatch, X-Ray, and IAM best practices. Collaborate with cross-functional teams to define, design, and More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯

and deploy ML models, algorithms and agentic AI systems to address complex business challenges across a range of sectors Cloud & MLOps Management: Lead the implementation of ML solutions on AWS cloud (with heavy use of Amazon SageMaker and related AWS services). Develop and maintain end-to-end CI/CD pipelines for ML projects, using infrastructure-as … code tools like AWS CloudFormation and Terraform to automate model deployment and system setup Project Leadership: Oversee the ML lifecycle from data preparation to model training, validation, and deployment. Make high-level design decisions on model architecture and data pipelines. Mentor junior engineers and collaborate with data scientists, ML engineers, and Software Engineering teams to ensure successful delivery of … PyTorch). Solid understanding of ML algorithms, model evaluation techniques, and optimisation. Experience with NLP techniques, generative AI or financial data modelling is advantageous Cloud & DevOps: Proven experience with AWS cloud services relevant to data science – particularly Amazon SageMaker for model development and deployment. Familiarity with data storage and processing on AWS (S3, AWS Lambda, Athena More ❯