Staff Engineer - Agentic AI

Position Summary

The Staff Engineer – GenAI is a hands‑on technical leader responsible for designing, building, and maintaining a large‑scale agentic AI platform that enables autonomous, AI‑driven solutions for the enterprise.

• Own the end‑to‑end development of the enterprise's Agentic AI platform.
• Design, develop, test, and deploy high‑performance generative AI capabilities that allow AI agents to autonomously understand, plan, and execute multi‑step tasks with minimal human oversight.
• Ensure the platform is scalable, highly available, and can support mission‑critical applications.
• Provide technical direction across the organization on GenAI‑related projects.
• Work closely with Solution and Enterprise Architects to develop solution architectures that integrate LLMs, agent frameworks, and AI services into the broader enterprise system.
• Lead by example in coding standards, prompt engineering, and context engineering best practices.
• Conduct code, prompt, and context‑pipeline reviews to ensure high code quality, readability, and robust test coverage.
• Establish guidelines for reproducible experiments and version control of prompts, contexts, agents, and datasets.
• Build internal frameworks and orchestration pipelines to integrate LLMs and agents with enterprise data sources and services.
• Leverage GenAI tools and protocols (e.g., MCP, A2A, function/tool calling) to enable high‑value GenAI business cases across the organization.
• Design retrieval, memory, and tool‑use patterns that make enterprise context reliably available to models at inference time.
• Drive continuous improvements in software development and LLM lifecycle processes.
• Implement LLMOps/GenAI Ops best practices such as automated evaluation, prompt and agent versioning, observability, guardrails, and CI/CD pipelines for prompt, agent, and model deployment.
• Evaluate new tools and methods (e.g., Lang Smith, Lang Fuse, Bedrock, or cloud‑based AI services) to enhance team efficiency and system reliability.
• Mentor and coach team members in advanced GenAI and software engineering techniques, fostering a culture of knowledge‑sharing and innovation.
• Provide input to management on team performance, hiring, and promotions, helping develop talent in generative AI expertise (non‑managerial feedback role).
• Perform other job‑related duties as assigned or apparent.

• 10+ years of experience in designing, developing, and deploying enterprise‑scale technology solutions, with at least two years focused on GenAI/LLM or software architecture initiatives.
• Demonstrated ability to design and manage complex platforms or products at scale.
• Deep understanding of Generative AI techniques and transformer‑based models (e.g., Claude, GPT, or other foundation and open‑source LLMs).
• Hands‑on experience integrating and adapting LLMs into enterprise applications, including expertise in prompt engineering, context engineering, retrieval‑augmented generation (RAG), GraphRAG, and agentic patterns (ReAct, planner/executor, multi‑agent orchestration).
• Familiarity with multi‑agent AI systems and agent‑based architectures is highly desirable.
• Proficiency in modern GenAI frameworks/libraries such as Lang Chain, Lang Graph, Semantic Kernel, or similar tools.
• Experience with model‑serving runtimes and agent orchestration frameworks for building and managing complex GenAI pipelines.
• Strong grasp of NLP fundamentals, tokenization, embeddings, and knowledge representation.
• Experience with multimodal models, function calling, structured output, and reinforcement learning from feedback (RLHF/RLAIF) is a plus.
• Solid experience in cloud architectures (AWS Bedrock, GCP AI, or other cloud‑based AI services) for scalable GenAI solution deployment.
• Familiarity with containerization and serverless architectures for deploying agents, retrieval services, and model gateways at scale.
• Understanding of LLMOps/AI Dev Ops practices (CI/CD for prompts and agents, eval‑driven development, prompt and model versioning, observability/tracing, cost and token monitoring, automated red‑teaming and guardrail enforcement).
• Strong background in data engineering and architecture for AI‑ready…