Data Architect $130,200 jobs

We are seeking an experienced Senior Generative AI Developer to help drive the design, development, and integration of state-of-the-art Generative AI and agentic AI solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models — not on training or fine-tuning models.

Key Responsibilities

• Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges.

• Architect advanced context engineering strategies — context layering, chaining, compression, pruning/offloading, and memory management — to maximize reliability, provenance, and token efficiency in production.

• Design and implement advanced generative AI methods, including sophisticated prompt engineering and Retrieval-Augmented Generation (RAG).

• Build and optimize RAG systems, including hybrid search, multi-vector retrieval, and re-ranking pipelines.

• Design and implement knowledge graphs and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.

• Architect agentic workflows and multi-agent systems using Google Agent Development Kit (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.

• Design robust agent harnesses — governance, constraints, feedback loops, state/session management, and execution controls that make long-running agent systems reliable and safe.

• Integrate agents with tools and data via the Model Context Protocol (MCP) and orchestrate inter-agent collaboration and task delegation via the Agent2Agent (A2A) protocol.

• Support the integration of GenAI and agentic applications into production environments, ensuring robust deployment, scalability, observability, and maintainability.

• Contribute to the development and optimization of real-time and streaming AI solutions.

• Stay current with the latest advances in generative and agentic AI and actively share knowledge with the team.

• Ensure adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.

• Mentor junior team members, provide code reviews, and foster a culture of technical excellence.

Required Technical Skills

• 4+ years of experience in AI/software development, including significant experience in Generative AI and agentic AI.

• Strong skills in Python and experience with data preprocessing, document ingestion, and API development.

• Deep, hands-on expertise in core generative AI concepts — foundation models, LLMs, embeddings, tokenization, and context-window management.

• Proficiency with major GenAI APIs (OpenAI, Gemini, Claude, etc.) and orchestration frameworks such as LangChain and LlamaIndex.

• Advanced skills in prompt engineering and context engineering, including familiarity with prompt design tools/frameworks and dynamic context orchestration.

• Proficiency with vector databases and embedding models for large-scale retrieval.

• Strong experience building RAG systems, including chunking strategies, hybrid search, and multi-vector retrieval.

• Practical experience designing knowledge graphs and Graph RAG pipelines (e.g., using graph databases such as Neo4j or ArangoDB) for relationship-aware, multi-hop retrieval.

• Proven experience building agentic AI systems with Google ADK and/or comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling, planning, and memory.

• Strong grasp of multi-agent orchestration patterns (supervisor/worker, hierarchical, peer-to-peer) and harness engineering (governance, feedback loops, execution controls, agent isolation/sandboxing).

• Hands-on experience with agent interoperability protocols — the Model Context Protocol (MCP) for tool/data access and the Agent2Agent (A2A) protocol for inter-agent collaboration.

• Experience with agent observability and evaluation (e.g., tracing, OpenTelemetry-based tooling) for production agent systems.

• Experience working with AWS (or equivalent) cloud infrastructure for AI/GenAI.

• Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for AI/agentic applications.

• Strong skills in NLP (NER, dependency parsing, text classification, topic modeling).

• Solid understanding of AI compliance, guardrails, and responsible AI practices.

• Demonstrated portfolio of successful AI-driven projects in a business environment.

Required Soft Skills

• Strong collaboration skills to work effectively in cross-functional teams.

• Analytical and proactive approach to problem-solving.

• Clear communication skills for both technical and non-technical audiences.

• Eagerness to learn, innovate, and mentor less experienced developers.

Education:

• Bachelor’s degree/University degree or equivalent experience

• Master’s degree preferred

  
  

This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.