Lead Gen AI Engineer (contract)

Responsibilities

• Design and develop Agentic AI systems using Google ADK, LangChain, and LangGraph, including multi-agent orchestration, state management, and tool integration leveraging enterprise approved LLMs.
• Integrate agents with enterprise Systems of Record (SoRs) by building reliable data pipelines, APIs, and connectors across structured and unstructured sources.
• Integrate organization-approved foundation models (like Anthropic, Google Gemini etc.) into Agentic task-based workflows.
• Partner with Process Excellence and Ops teams to ideate and implement AI copilots and AI Agents for business functions.
• Develop scalable Python-based services for agent workflows, incorporating RAG, tool calling, memory, and structured outputs.
• Engineer data ingestion and transformation pipelines (batch/streaming) to enable high-quality, governed data access for AI agents.
• Write and optimize complex SQL queries for analytics, feature extraction, and real-time agent decisioning.
• Implement observability, evaluation, and guardrails across both data and AI layers, ensuring performance, quality, compliance, and cost efficiency.
• Use SQL, Python, and cloud-native tools (GCP, Azure, or AWS) to ensure data quality and lineage.

Qualifications

• Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
• 5+ years in Gen AI, AI Data Engineering, and Agentic AI-focused roles.
• Advanced Prompt Engineering, Context Engineering skills, Python skills.
• Hands-on experience building agentic AI solutions using Google ADK + LangChain/LangGraph, including orchestration and tool usage patterns.
• Strong Python development skills for backend services, workflow engines, and AI pipelines.
• Solid data engineering expertise: Building ETL/ELT pipelines, Integrating data from multiple SoRs (APIs, DBs, files, streams), Working with data quality, schema evolution, and lineage.
• Advanced SQL proficiency (complex joins, window functions, query optimization).
• Experience with RAG architectures and integrating LLMs with enterprise data sources (vector stores + relational systems).
• Production-grade engineering practices: testing, CI/CD, logging, monitoring, and error handling.

Desired Skills

• Experience with modern data stack tools (e.g., dbt, Airflow/Composer, Kafka/PubSub, BigQuery/Snowflake).
• Familiarity with vector databases and hybrid retrieval strategies.
• Experience deploying solutions on GCP (preferred) or other cloud platforms with scalable architectures.
• Knowledge of data governance, security, and PII handling in AI/data pipelines.
• Exposure to LLMOps frameworks (evaluation, prompt/version management, tracing, cost optimization).
• Experience implementing guardrails and safety controls for enterprise AI agents.