Highly skilled and experienced Senior AI, Machine Learning, and Automation Expert to join our dynamic team. The ideal candidate will have a strong background in AI, machine learning, and automation technologies, with a proven track record of implementing and managing complex projects. This role requires a deep understanding of various AI and ML tools, as well as expertise in DevOps practices to ensure seamless integration and deployment of AI solutions. Strategic Leadership & Architecture: Define GenAI strategy and roadmap: Align GenAI initiatives with business priorities, identifying opportunities for high-value automation, augmentation, and transformation. Solution architecture ownership: Design secure, scalable, and cost-effective GenAI architectures using AWS Bedrock for model orchestration and integration. Tool & model evaluation: Select appropriate foundation models (e.g., Anthropic Claude, Amazon Titan, Meta Llama, Stability AI) based on business use cases, data privacy, and latency needs. AI governance and compliance: Establish guardrails for responsible AI, data governance, and model lifecycle management in AWS environments. Architecture patterns: Define and enforce reusable architecture blueprints (e.g., RAG, agentic workflows, chat-with-enterprise-data patterns). Hands-on Technical Expertise (AWS & GenAI Stack): AWS Bedrock expertise: Deploy and integrate foundation models using the Bedrock API. Configure model parameters, embeddings, and inference pipelines. Manage cross-model orchestration and multi-agent workflows. RAG implementation: Design retrieval-augmented generation pipelines using Amazon Kendra, OpenSearch, or vector databases (e.g., Pinecone, Weaviate, FAISS). Build context-aware conversational systems integrating enterprise data. Agent development: Create custom AI agents and copilots using AWS Bedrock Agents or LangChain with Bedrock integration. Leverage AWS Lambda, Step Functions, or SageMaker for agent orchestration and automation. Integration and APIs: Build APIs and applications connecting Bedrock with AWS services like S3, DynamoDB, Redshift, Aurora, and EventBridge. Integrate GenAI features into enterprise tools (e.g., ServiceNow, Jira, Salesforce). Prompt engineering & tuning: Optimize prompt templates and chain-of-thought frameworks for model accuracy and reliability. Model customization: Fine-tune or adapt foundation models with domain-specific data via Bedrock or SageMaker. Leadership, Collaboration & Delivery: Lead cross-functional AI delivery teams (data engineers, ML engineers, cloud architects, developers). Translate business needs into AI solutions with clear ROI, feasibility, and delivery timelines. Mentor and enable engineers on AWS Bedrock and GenAI frameworks (LangChain, Semantic Kernel, LlamaIndex). Communicate technical strategy to senior leadership, articulating value and innovation. Drive PoCs and pilots to production-grade AI systems across use cases like knowledge assistants, automation bots, or predictive analytics.