Our Client is seeking a Staff Engineer (Agentic AI) .
Duties Include:
- 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, ensuring alignment with Enterprise Architecture principles and non-functional requirements (security, scalability, resilience, token economics, and latency budgets)
- 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-including evals, regression suites, and golden datasets for LLM pipelines and API integrations; Establish guidelines for reproducible experiments and version control of prompts, contexts, agents, and datasets (e.g., using Git and LLMOps tools)
- 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
- Implement LLMOps/GenAI Ops best practices such as automated evaluation, prompt and agent versioning, online/offline evals, observability (traces, token usage, hallucination and grounding metrics), guardrails, and CI/CD pipelines for prompt, agent, and model deployment
Requirements
- 10+ years of experience in designing, developing, and deploying enterprise-scale technology solutions, ideally with two years focused on GenAI/LLM or software architecture initiatives; Demonstrated ability to design and manage complex platforms or products at scale
- 7 – 10 years of progressively complex experience building and scaling enterprise software systems-combined with recent, hands-on application of GenAI or Agentic AI technologies in production environments
- 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) for grounding outputs in enterprise data; Familiarity with multi-agent AI systems and agent-based architectures is highly desirable
- Proficiency in modern GenAI frameworks/libraries such as LangChain, LangGraph, 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
- Strong background in data engineering and architecture for AI-ready data-skilled in curating, chunking, enriching, and governing unstructured and semi-structured corpora for LLM consumption; Hands-on experience with vector databases (e.g., pgvector, Elastic Search), hybrid search, reranking, knowledge graphs, and embedding strategies for high-quality retrieval
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