GenAI / AI-ML Engineer

 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.• Design end-to-end AWS architectures using appropriate managed AI/ML, compute, data, integration and security services.• Implement security, access control, logging, monitoring, observability and operational controls for production AI systems.• Deploy and operate AI/ML solutions on AWS and troubleshoot latency, scalability, reliability, model quality, infrastructure and cost issues.• Monitor deployed ML models for performance, drift, reliability and business-aligned metrics.• Collaborate with data engineers, software engineers, architects and product teams to build reliable production solutions.• Produce reusable components, technical documentation, architecture decisions and implementation guidance for delivery teams.