25 of 25 Remote/Hybrid Amazon SageMaker Jobs

extend a modern data pipeline, and contribute to building scalable AI infrastructure using cloud-based AI/ML services such as AWS Bedrock/Sagemaker, GCP Vertex AI, TensorFlow, PyTorch. Location: Belfast, NI (Hybrid) or Remote UK Responsibilities Design and build production-ready AI/ML systems, with … standard model-powered features in production (chatbots, copilots, RAG systems, agents) Proficient in working with traditional cloud AI/ML platforms such as Amazon Sagemaker, GCP Vertex AI, or Azure ML and frameworks such as TensorFlow, PyTorch, scikit-learn. Education and Experience 7+ years of professional software engineering ...

/CD pipelines. You can set up APIs, write robust code, and debug pipelines. ● Experience with cloud-native ML workflows, including AWS Sagemaker, Lambda, ECS, and containerisation with Docker. ● Familiarity with frontend collaboration—how your model outputs can drive dynamic UIs, content personalization, or editor tools. ● Comfort in “hacking ...

consultancy helping enterprise organizations unlock value through cloud, analytics, machine learning, and intelligent automation. Our partnerships include leading platforms such as Snowflake, Amazon Web Services, Microsoft Azure, Google Cloud, Databricks, dbt Labs, and Fivetran. We are a remote-first organization with team members across the United States, Latin America … account growth initiatives Experience designing and deploying AI/ML solutions in production Strong pre-sales, proposal development, or expansion success Deep expertise with Amazon Web Services, Google Cloud, Microsoft Azure, Databricks, Vertex AI, or Amazon SageMaker Strong coding skills in Python, Java, Scala, or similar Experience ...

engineering and automation. Experience with Databricks or Apache Spark for big data processing. Experience with at least one major cloud ML platform, eg AWS Sagemaker Advanced SQL capabilities for data manipulation, querying, and optimization. Proven experience with workflow orchestration tools (e.g., Airflow, AWS Step Functions). Solid understanding ...

deploying ML models in Python using frameworks such as scikit-learn, XGBoost, PyTorch, or TensorFlow Strong experience with cloud-based ML deployment (e.g., AWS SageMaker, Lambda, S3) Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering ...

best practices to build robust data pipelines and deploy models at scale. You'll use technologies like Python (and Clojure), AWS services (Athena, Bedrock, SageMaker, etc.), dbt, Prefect, and CI/CD automation with monitoring to ensure models are reliable and up-to-date. Collaborate across teams of data ...

related field Hands-on experience with AI frameworks such as LangChain, LlamaIndex, TensorFlow, or PyTorch Expertise deploying solutions on cloud platforms (AWS Bedrock/SageMaker or Google Cloud Vertex AI) Experience working with modern data tooling (e.g., dbt, Airflow) or similar frameworks for data transformation and workflow orchestration ...

models, including data validation, correctness checks, and reproducibility. Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker, Databricks, Unity Catalog, Snowflake/Snowpark) to build scalable ML data pipelines and model workflows. Strong collaboration skills to work effectively with ML scientists ...

facing or product-centric ML systems. A product-focused mindset, balancing experimentation with real-world value. Useful additional experience includes: AWS or GCP environments (SageMaker, Bedrock, Lambda, S3, Redshift). MLOps practices (monitoring, deployment, optimisation). Working with streaming data, transformer models or recommendation systems. What They Offer ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

TensorFlow/Keras . Language & Data: advanced Python (NumPy, Pandas, Scikit-learn) and experience with Hugging Face Transformers. Cloud & MLOps: experience with AWS (SageMaker), Google Cloud (Vertex AI), or specialized tools like Weights & Biases and LangChain. Vector Databases: familiarity with Pinecone, Milvus, or Weaviate for RAG evaluation. Why Prolific ...

development, including tools such as OpenAI, Copilot, or JetBrains AI Assistant. • Experience integrating LLMs into applications. • Familiarity with cloud-based services such as AWS (SageMaker, Bedrock, Lambda) or equivalent platforms. • Experience with asynchronous frameworks (Aiohttp, FastAPI, or similar). Tech Stack • Languages & Frameworks: Python, PHP, JavaScript, Vue.js, HTML5, CSS3. ...

Airflow) and data platforms (Snowflake, PostgreSQL, Airbyte), and building scalable pipelines that support ML data processing and model workflows. Advanced experience with AWS (including SageMaker and S3), ML infrastructure frameworks such as MLflow and Terraform, and exposure to platforms like Databricks, with a proven track record of implementing ...

Science, Engineering, or a related field Experience across multiple cloud platforms (Azure, GCP) Hands-on experience with AWS AI/ML services, including Bedrock, SageMaker, and Kendra Familiarity with AWS agent frameworks and orchestration tooling, including Step Functions and Amazon Q Experience designing or deploying RAG architectures ...

translate business needs into technical solutions Required Skills & Experience: Active SC Clearance (mandatory) Strong proficiency in Python Hands-on experience with AWS , particularly SageMaker Experience with Streamlit for rapid prototyping and PoC development Solid understanding of anomaly detection techniques Experience building data workflows/pipelines Desirable: Experience working ...

Experience with unit, integration, and pipeline-level testing for ML models, including data validation, correctness checks, and reproducibility. Familiarity with cloud platforms (AWS preferred: SageMaker, S3, EC2) and reproducible ML pipelines. Experience with CI/CD, Orchestration tools (Airflow, MLflow, Kubernetes, Terraform) and ML/data platforms(SageMaker ...

complex business requirements into robust data solutions. The Role: You will tackle complex data challenges in a fast-paced, exciting environment, leveraging cutting-edge Amazon Web Services (AWS) technologies such as Lambda, SageMaker and Bedrock to develop innovative and scalable data solutions. RealityMine has an out of hours ...

governance Translate business objectives into modelling strategies aligned to risk appetite and operational constraints Build and deploy models using Python, SQL, and AWS (SageMaker or equivalent) Partner closely with Engineering, Data, and Risk/Financial Crime teams to ensure robust, production-ready solutions Establish monitoring frameworks for performance, drift … closely with Engineering/Data teams in production-first environments Comfortable operating within regulated industries Desirable AWS experience (S3, Athena/Glue, IAM, Lambda) SageMaker or equivalent ML platform experience Financial services domain knowledge (risk, fraud, affordability, payments) Experience with model explainability and governance documentation Package & Benefits Hybrid working ...

experience. Alongside this, you’ll work across a modern cloud stack — integrating with AWS services , including serverless architecture and elements of machine learning via SageMaker . Key Responsibilities Enhance, debug, and deliver a high-quality React Native (TypeScript) mobile app Take ownership of features from development through to release … experience Integrate the app with backend services and APIs Work with AWS (Lambda) to support scalable, serverless functionality Collaborate on ML-driven features leveraging SageMaker (Python) Contribute to architecture decisions and engineering best practices Tech Stack React Native (TypeScript) AWS Lambda (serverless architecture) SageMaker (Python – ML integration) What ...