Senior Delivery Consultant -Data Analytics & GenAI, AWS Professional Services Public Sector Job ID: Amazon Web Services Australia Pty Ltd Are you a Senior Data Analytics and GenAI consulting specialist? Do you have real-time Data Analytics, Data Warehousing, Big Data, Modern Data Strategy, Data Lake, Data Engineering and GenAI experience? Do you have senior stakeholder engagement experience to support … decisions and desired customer outcomes. Key job responsibilities Expertise: Collaborate with pre-sales and delivery teams to help partners and customers learn and use services such as AWS Glue, Amazon S3, Amazon DynamoDB, Amazon Relational Database Service (RDS), Amazon Elastic Map Reduce (EMR), Amazon Kinesis, Amazon Redshift, Amazon Athena, AWS Lake Formation, Amazon DataZone, AmazonSageMaker, Amazon Quicksight and Amazon Bedrock. Solutions: Support pre-sales and deliver technical engagements with partners and customers. This includes participating in pre-sales visits, understanding customer requirements, creating consulting proposals and creating packaged data analytics service offerings. Delivery: Engagements include projects proving the use of AWS services to support new distributed computing More ❯
you will be responsible for driving strategy, top line revenue growth and overall end customer adoption of our comprehensive GenAI/ML solutions. AI/ML services such as Amazon Nova, AmazonSageMaker, Amazon Bedrock, and our generative AI offerings. You will lead a senior go-to-market team across 100+ countries in EMEA, leveraging sales … on thinking big, delivering exceptional results for our customers, and working across AWS as to build the future of AI and cloud computing. This position is part of the Amazon Specialist and Partner Organization (ASP). Specialists own the end-to-end go-to-market strategy for their respective technology domains, providing the business and technical expertise to help … candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most More ❯
candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating - that's why customers from the most … and orchestration approaches Hands-on experience with model customization techniques such as fine-tuning, continued pre-training, and LLM-as-judge evaluation Experience with optimization of models on GPUs, Amazon Silicon, or TPUs, also experience with open source frameworks for building applications powered by LLMs like LangChain, LlamaIndex, and/or similar tools Experience building generative AI applications on … AWS using services such as Amazon Bedrock and AmazonSageMakerAmazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of More ❯
scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP AI Platform, Azure ML, or equivalent). Solid understanding of data-engineering concepts: SQL/noSQL, data pipelines (Airflow, Prefect, or similar), and batch/streaming frameworks (Spark More ❯
ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on experience with MLOps tools (MLflow, SageMaker, Kubeflow, etc.) and version control systems. Strong knowledge of APIs, microservices architecture, and CI/CD pipelines. Proven experience in leading teams, managing stakeholders, and delivering end-to-end More ❯
and logging tools to track system performance and model effectiveness in production environments. Familiarity with MLOps Tools: Knowledge of various MLOps tools and platforms, including MLflow, Databricks, Kubeflow, and SageMaker, to streamline the machine learning lifecycle. Version Control Systems: Proficient in using version control systems such as Git to manage code and collaborate with development teams. Software Testing and More ❯
create measurable business impact across the organization. To be successful in this role, you will need the following: Strong proficiency and experience in AWS based Machine Learning technologies (AWS SageMaker, Lambda, S3, EC2). Experience with CI/CD pipelines and Docker containerisation for ML model development/deployment. Data preprocessing and feature engineering expertise. Programming skills in Python More ❯
for data insights Data Bricks/Data QISQL for data access and processing (PostgreSQL preferred, but general SQL knowledge is important) Latest Data Science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., TensorFlow, MXNet, scikit-learn) Software engineering practices (coding standards, unit testing, version control, code review) Hadoop distributions (Cloudera, Hortonworks), NoSQL databases (Neo4j, Elastic), streaming technologies (Spark More ❯
Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow, Spark) Proficient in both SQL (PostgreSQL, MySQL) and NoSQL (Elasticsearch, MongoDB More ❯
Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation) systems at scale Strong experience with MLOps practices and model deployment pipelines Proficient in cloud AI services (AWS SageMaker/Bedrock) Deep understanding of distributed systems and microservices architecture Expert in data pipeline platforms (Apache Kafka, Airflow, Spark) Proficient in both SQL (PostgreSQL, MySQL) and NoSQL (Elasticsearch, MongoDB More ❯
top-tier performance. Collaborating with cross-functional teams (data scientists, software engineers, product managers) to turn business needs into technical solutions. Building and maintaining scalable ML pipelines using AWS (SageMaker, EC2, S3, Lambda, Rekognition). Staying up to date with the latest in ML and computer vision research and applying new techniques to real-world challenges. Contributing to the More ❯
for data/AI), SQL Cloud & DevOps: AWS/Azure/GCP services, CI/CD pipelines, Docker/Kubernetes AI/ML Tooling: Familiarity with cloud AI services (SageMaker, Vertex AI, Azure AI) and ML lifecycle management Data: Relational & NoSQL databases, data modelling, ETL/ELT, BI tools Strategic thinker with commercial acumen; can link technology to bottom More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Willis Global
for data/AI), SQL Cloud & DevOps: AWS/Azure/GCP services, CI/CD pipelines, Docker/Kubernetes AI/ML Tooling: Familiarity with cloud AI services (SageMaker, Vertex AI, Azure AI) and ML lifecycle management Data: Relational & NoSQL databases, data modelling, ETL/ELT, BI tools Strategic thinker with commercial acumen; can link technology to bottom More ❯
Fitch Group, Inc., Fitch Ratings, Inc., Fitch Solutions Group
classification, decision trees, support vector machines, and neural networks (deep learning experience strongly preferred) Knowledge of popular Cloud computing vendor (AWS and Azure) infrastructure & services e.g., AWS Bedrock, S3, SageMaker; Azure AI Search, OpenAI, blob storage, etc. Bachelors degree (masters or higher strongly preferred) in machine learning, computer science, data science, applied mathematics or related technical field What Would More ❯
to support decisions across multiple business areas, including marketing, sales, claims, retention, customer behavior, fraud detection, and customer servicing. Deploy machine learning models into production using AWS services, including SageMaker, S3, Feature Store, ensuring scalable, reliable, and monitored solutions that directly support key business processes. Collaborate with product management and engineering teams to integrate data-driven insights and deploy More ❯
and Python, understanding of data structures, algorithms, and software design patterns. Experience with AI/Gen AI frameworks like TensorFlow or PyTorch. Experience with cloud platforms such as AWS SageMaker or Azure Machine Learning. Ability to translate business problems into solutions. Strong communication skills; bilingualism/multilingualism is a plus. Willingness to travel (50% - national and international). Our More ❯
City of London, London, United Kingdom Hybrid / WFH Options
Omnis Partners
AI assistants and agentic tools to maximise impact. Bonus points if you’ve played with: LangGraph, AutoGen, or other agentic AI frameworks LoRA fine-tuning, RLHF, or vLLM AWS (SageMaker, EKS), Docker, or speech-to-text systems 🌟 What You Get: 100% remote role (UK or Spain) Work on real-world LLM deployments in an agile, experimental environment More ❯
AI assistants and agentic tools to maximise impact. Bonus points if you’ve played with: LangGraph, AutoGen, or other agentic AI frameworks LoRA fine-tuning, RLHF, or vLLM AWS (SageMaker, EKS), Docker, or speech-to-text systems 🌟 What You Get: 100% remote role (UK or Spain) Work on real-world LLM deployments in an agile, experimental environment More ❯
Python Experience with machine learning, familiar with Huggingface, Pytorch, and similar ML tools and packages Familiarity with deploying and scaling ML models in the cloud, particularly with AWS and SageMaker Understanding of DevOps processes and tools: CI/CD, Docker, Terraform, and monitoring/observability Bonus: experience with vector databases, semantic search, or event-driven systems like Kafka Additional More ❯
wed like to see from you: Extensive experience designing and deploying ML systems in production Deep technical expertise in Python and modern ML tooling (e.g. MLflow, TFX, Airflow, Kubeflow, SageMaker, Vertex AI) Experience with infrastructure-as-code and CI/CD practices for ML (e.g. Terraform, GitHub Actions, ArgoCD) Proven ability to build reusable tooling, scalable services, and resilient More ❯
