As an experienced engineer, you know how to design, develop, and deliver production-grade agentic AI systems that demonstrate the practical value of generative AI, large language models (LLMs), and autonomous workflows. This role combines deep technical expertise with strong product skills to design AI applications that leverage prompting, retrieval-augmented generation (RAG), agentic orchestration, evaluation pipelines, and human-in-the-loop systems to deliver measurable impact.

Design adaptable agentic AI architectures that support multiple model providers, tool ecosystems, modalities, and deployment modes.

Build modular and reusable components for prompting, retrieval, orchestration, tool execution, memory management, and evaluation to enable rapid development of new AI capabilities.

Integrate LLMs, embeddings, RAG pipelines, structured outputs, and long-context or memory mechanisms into production-ready systems.

Apply advanced prompting techniques such as few-shot, chain-of-thought, tool-calling, and function-calling, orchestration frameworks such as LangChain or equivalent, and agentic architectures such as MCP, A2A, or similar patterns, to enable goal-directed autonomy with guardrails, observability, and human oversight, including planning, tool use, delegation, and recovery from failure.

Design and implement evaluation frameworks, both offline and online, to measure correctness, robustness, safety, and business impact of AI systems.

Optimize models and workflows for cost, latency, reliability, and scalability, using systematic benchmarking and experimentation.

Develop data pipelines for ingestion, cleaning, chunking, embedding, indexing, and continuous refresh of structured and unstructured data for RAG and memory systems.

Combine text, audio, vision, and other modalities in unified processing workflows, including document understanding, transcription, summarization, and cross-modal reasoning.

Leverage vector databases, hybrid search, reranking, and retrieval optimization techniques to enhance grounding and reduce hallucination in RAG systems.

Incorporate guardrails, safety filters, access controls, and monitoring mechanisms to ensure responsible and secure deployment of agentic AI systems.

Deploy AI services securely and at scale on AWS or equivalent cloud platforms.

Use containerizing, including in Docker or Kubernetes, or serverless approaches for flexible deployment.

Apply CI/CD and eval-driven development best practices for AI systems, including automated testing of prompts and workflows, versioning of prompts and agents, and safe rollout of model updates.

Use asynchronous programming and event-driven patterns to support scalable, long-running, or multi-agent workflows.

Leverage modern build and packaging workflows to deliver optimized, portable application artifacts.

Use AI assistance tools to accelerate development, debugging, and system design while maintaining engineering rigor and code quality.

Collaborate with clients to identify high-value AI opportunities and define solution requirements.

Present AI capabilities and technical solutions to both technical and non-technical stakeholders.

Lead workshops and prototyping sessions to accelerate adoption.

Provide guidance on responsible AI practices, ethics, and compliance.