AI Engineer

About the role

We’re seeking a AI Engineer to design and ship production-grade agentic AI systems that automate complex workflows end-to-end. This is a hands-on role with significant technical ownership. You’ll work closely with the Chief Architect, product, engineering, and domain experts to translate ambiguous, high-impact problems into reliable AI-driven user experiences.

What success looks like:

Ship AI capabilities that measurably improve user outcomes (quality, time saved, throughput)

Build systems that are reliable by design: evals, observability, safety, and cost/latency controls from day one

Iterate quickly using a tight loop of instrument → evaluate → improve → deploy

What You’ll Do Agentic AI Feature & Workflow Development

- Build and integrate AI-driven features using LLM APIs (OpenAI / Azure OpenAI, Anthropic, Gemini on Vertex AI)

- Design and implement tool-using agents (structured function calling, schema validation, retries, fallbacks)

- Build multi-agent workflows when appropriate (e.g., planner/worker, reviewer/critic, specialist routing) and know when a simpler architecture is better

- Create agentic workflows such as document understanding, extraction, reasoning over evidence, task automation, and multi-step decision support

- Own context engineering end-to-end:

- dynamic context assembly (retrieval + state + tool outputs)

- context budgeting and compression/summarization

- grounding strategies to reduce hallucinations and improve consistency

- Implement retrieval-augmented generation (RAG) and search workflows using off-the-shelf vector stores and embedding services

Evaluation, Quality & Iteration (Core)

- Establish evaluation frameworks for accuracy, reliability, and output quality

- Build task-specific eval suites: golden datasets, adversarial cases, regression tests, and rubric-based scoring

- Set up automated evaluation pipelines and release gates (CI/CD-friendly) tied to prompt/model/version changes

- Define and monitor online metrics (e.g., task success rate, human override rate, safety flags, latency, cost) and run experiments/A-B tests where appropriate

- Use LLM-as-judge responsibly: calibrate, validate, and pair with human labels when needed

Engineering, Integration & Observability

- Develop scalable backend services and APIs that incorporate AI functionality

- Integrate AI pipelines into existing cloud, microservices, and event-driven architectures

- Implement observability and analytics for all AI features (tracing, evaluations, prompt versioning, cost tracking) Example tooling: Langfuse (and/or OpenTelemetry-compatible stacks)

- Ensure reliability, uptime, performance, and security of AI services

- Build internal tooling for evaluation, testing, prompt/version management, and safe deployment