AI Native SW Engineer

You build the systems that actually make AI work in enterprise environments, not demos, not prototypes that stall after a pilot, but production agentic architectures running inside real client organizations. The difference between an AI Engineer and what we are looking for is straightforward: you have shipped a multi agent system in production, you have owned the eval harness, and you know what happens when your agent fails at 2am because you have lived it.

Key Responsibilities

• Design and build production grade agentic systems end to end: multi agent orchestration, RAG pipelines, policy based routing, tool invocation, memory management, and lifecycle observability.
• Build and own RAG pipelines: embeddings, chunking strategy, vector search, context window engineering and tuning against real quality targets.
• Integrate and abstract across multiple LLM providers - OpenAI, Anthropic, Vertex AI, and open source models - with fallback routing, token, cost, and latency management.
• Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), cost and safety monitoring.
• Embed directly with client engineering teams to design, prototype, and deploy agentic solutions - workshops, proofs of concept, code with sessions, and architecture walkthroughs.
• Build reusable patterns, accelerators, and playbooks that scale beyond the individual client engagement and enable the next one to start faster.
• Define and use metrics to measure agent accuracy, latency, safety, and cost effectiveness; present findings and recommendations to client stakeholders in business terms.

Qualifications

• Software engineering experience in production environments.
• Minimum 1 year of hands on experience designing and deploying agentic AI solutions in a production environment - non negotiable.
• Demonstrated experience with agentic orchestration frameworks: LangGraph, CrewAI, AutoGen, or equivalent - at production depth, not tutorial level.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code: provider abstraction, token management, latency and cost tradeoffs.
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning, and production observability.
• Cloud native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).
• Strong Python; Java or equivalent backend language acceptable; production debugging and observability experience.
• Quality of experience is weighted over years; a candidate who has shipped three production agentic systems in four years is preferred over a generalist with passive AI exposure.

Equal Employment Opportunity Statement

We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, sexual orientation, gender identity or expression, marital status, citizenship status or any other basis as protected by applicable law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.