Applied AI Developer - Hybrid - (Face - to - Face interview Mandatory)

Job Title: Applied AI Developer

Locations: Palo Alto, CA; Austin, TX; Reston, VA; NYC, NY; Morrisville, NC; Seattle, WA; and Lawrence, KS (Hybrid)

Duration: Long-term

An in-person interview must

Job Summary:

Client is expecting resources to be onsite minimum of 3 days and 2 days remote.

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 clients 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.

Job Responsibilities:

• 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.

Required Skills:

• 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., LangGraph, Google ADK, Mastra, Claude Agent SDK), observability and evals tooling (e.g., Langfuse, LangSmith, 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 (Google Cloud Platform and/or AWS) — compute, storage, networking, and managed AI services.
• Experience designing and integrating with enterprise APIs (REST, GraphQL), including authentication and authorization patterns (OAuth2, SAML, API keys, RBAC). Comfortable working with backend databases (SQL and NoSQL) — writing queries, understanding data models, and building data access layers that enforce role-based access control.
• Strong cross-functional collaborator and communicator, able to partner with Product, Operations, and domain experts to deliver end-to-end systems with measurable real-world impact.
• A force multiplier on the team — you raise the bar for clarity of thinking, system design standards, and team execution.

Nice to Have:

• Experience with AI evaluation tooling (Langfuse, LangSmith, Braintrust, or custom eval frameworks).
• Experience building custom MCP servers, not just consuming them.
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• AI-native builder with high velocity and ownership — intellectual curiosity, rapid adoption of new tools, bias to action, and the ability to drive ambiguous problems from concept to production.
• Hands-on experience with inference cost optimization — managing spend as agent deployments scale.
• Experience using AI-powered coding agents (e.g., Claude Code, GitHub Copilot, Cursor, Windsurf) to accelerate development workflows — rapid prototyping, code generation, debugging, and test writing.
• Experience with RAG (Retrieval-Augmented Generation) architectures and document retrieval pipelines — vector databases, embedding models, chunking strategies, and hybrid search — for building agents that answer questions grounded in enterprise documentation.