GenAI / AI-ML Engineer

Role summary

We are seeking an experienced GenAI / AI-ML Engineer to design, build, deploy and operate production-grade AI/ML and Generative AI solutions on AWS. The role requires strong Python engineering, solid understanding of conventional machine-learning algorithms, practical experience with LLMs, RAG and agentic AI, and strong hands-on expertise with AWS AI/ML and Generative AI services.

The engineer will be responsible for taking AI solutions from problem definition and solution design through development, evaluation, production deployment and optimization. The candidate should be capable of making architecture and technology decisions based on scalability, latency, reliability, security, model quality and cost.

The role also requires a strong understanding of LLM token consumption, model pricing and AWS service/infrastructure costing, with the ability to design solutions that balance business requirements, technical performance and operational cost.

Key responsibilities

• Translate business problems into measurable AI/ML and Generative AI objectives, solution approaches and evaluation criteria.

• Design and implement production-grade LLM, RAG, conversational AI and agentic AI solutions on AWS.

• Build RAG pipelines including document ingestion, chunking, embeddings, metadata filtering, vector retrieval, reranking and generation.

• Design and implement Amazon Bedrock Agents and Bedrock AgentCore-based agentic solutions, including tool use, memory/state, runtime execution and orchestration as applicable.

• Work with Amazon Bedrock foundation models and evaluate models based on quality, latency, token consumption and cost.

• Build integrations between LLMs/agents and enterprise APIs, databases, applications, tools and knowledge sources.

• Develop and evaluate supervised, unsupervised and deep-learning models based on business requirements.

• Apply appropriate machine-learning algorithms for classification, regression, clustering, anomaly detection and other relevant use cases.

• Perform data preprocessing, feature engineering, model training, validation, hyperparameter tuning and error analysis.

• Use Amazon SageMaker for appropriate ML development, training, experimentation, deployment and model lifecycle requirements.

• Establish evaluation mechanisms for LLM response quality, retrieval quality, groundedness, hallucination, latency and cost.

• Analyze and optimize LLM input/output token consumption, context size and model selection to control GenAI costs.

• Estimate and optimize AWS infrastructure and service costs across model inference, compute, storage, APIs, vector search and other components.