AI Developer

DivIHN (pronounced “divine”) is a CMMI ML3-certified Technology and Talent solutions firm. Driven by a unique Purpose, Culture, and Value Delivery Model, we enable meaningful connections between talented professionals and forward-thinking organizations. Since our formation in 2002, organizations across commercial and public sectors have been trusting us to help build their teams with exceptional temporary and permanent talent. Visit us at  to learn more and view our open positions.

Please apply or call one of us to learn more For further inquiries regarding the following opportunity, please contact our Talent Specialist, Deepak at  or Dinesh at

Title:

AI Developer

Location:

Palo Alto, CA Duration: 3 Months Only W2 candidates are eligible for this position. Third-party or C2C candidates will not be considered.

Job Description:

About the Team The Enterprise AI team is our client's internal AI enablement engine. They evaluate where AI can make a real difference, build the platforms and patterns that make adoption easy, and help engineering teams across the organization work smarter and faster. They operate at the intersection of applied AI, distributed systems, and enterprise operations their job is to make Client more efficient, one AI-powered workflow at a time.

This is a high-impact, high-autonomy team where you'll work closely with engineering, product, and operations teams across our client to bring AI capabilities to life. About the Role As an Applied AI Developer on the Enterprise AI team, you'll evaluate where AI fits, build the tools and platforms that make it practical, and enable teams across our client to adopt modern AI development patterns including LLM orchestration, agentic workflows, and model governance.

You stay hands-on and outcomes-oriented: prototyping, evaluating emerging AI technologies, and shipping solutions that make the organization measurably more efficient. What You'll Do Think AI-first assess where agentic approaches genuinely outperform conventional solutions, then own the quality bar: build automated evals, simulation tests, and regression frameworks that keep our AI systems reliable and improving as they scale. Design agentic systems tool orchestration, agent reasoning, memory, MCP integrations, and human-in-the-loop workflows.

Define and implement AI governance patterns guardrails, data lineage, auditability, and responsible AI practices that ensure our agentic systems are safe, compliant, and trustworthy. Drive adoption through pilots, proofs-of-concept, and scalable implementations across engineering teams. Collaborate with various business functions, product, security, and platform teams to translate AI use cases into production-grade, end-to-end solutions. What We're Looking For 5+ years of professional software engineering, with at least 2 years focused on applied AI in production systems.

Proficient in Python and/or Go; comfortable reading and writing in the other. Proven experience building and scaling multi-agent or agent-driven systems in production real-world operational ownership, not just simple LLM workflows. Hands-on experience with modern agent ecosystems, including frameworks (e.g., Lang Graph, Google ADK, Mastra, Claude Agent SDK), observability and evals tooling (e.g., Langfuse, Lang Smith, Braintrust), MCP implementations, and leading AI SDKs (e.g., OpenAI, Anthropic).

Strong systems and backend architecture fundamentals designing scalable, reliable systems and handling infrastructure, performance, failure modes, cost, and deployment concerns. Good understanding of cloud-native environments (GCP and/or AWS) compute, storage, networking, and managed AI services. Experience designing and integrating with enterprise APIs (REST, GraphQL)…