Senior AI Engineer

We’re seeking a Senior AI Engineer to design and ship production-grade agentic AI systems that automate complex workflows end-to-end. This is a hands-on, senior 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

• 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

Product & Collaboration

• Partner with product managers, designers, the Chief Architect, and domain SMEs to shape AI-first solutions

• Rapidly prototype concepts and iterate based on user feedback and measurable eval results

• Translate business problems into well-structured AI workflows without requiring ML model training

• Document system behavior, known failure modes, and operational playbooks

Governance & Safety

• Implement guardrails, checks, and fallback logic for safe and predictable AI behavior

• Help define and follow compliance, privacy, and responsible AI guidelines

• Design for safe tool execution (bounded actions, permissions, escalation paths, human-in the-loop review

What You Bring

• Strong software engineering background (Python preferred) and experience shipping backend services

• Deep hands-on experience building agentic LLM systems from first principles: agent loops, tool interfaces, planning/replanning, memory/state, and failure handling

• Strong context engineering ability: retrieval strategies, routing, grounding, context budgeting, and long-context tradeoffs

• Strong evaluation discipline: golden datasets, regression gating, automated eval pipelines, and online monitoring

• Practical experience with LLM APIs (OpenAI/Azure OpenAI/Anthropic/Gemini) and AI orchestration frameworks

• Excellent debugging, systems thinking, and problem decomposition skills

• Comfortable operating in fast-paced, ambiguous environments with high ownership

Signals We Value

• You’ve shipped an LLM/agent system in production and can clearly explain:

• the failure modes you discovered

• the evals you built to catch regressions

• how you improved cost/latency while increasing quality

• how you monitored and iterated safely over time

• You keep up with industry developments (model releases, frameworks, best practices) and can translate them into pragmatic improvement

Nice to Have

• Experience with cloud platforms (AWS and/or GCP), microservices, and event-driven systems

• Experience with observability stacks (OpenTelemetry, Datadog, Honeycomb) and AI-specific tooling (e.g., Langfuse, Braintrust, HumanLoop, W&B Weave)

• Experience with workflow orchestration for long-running jobs (Temporal, Celery, Airflow)

• Experience building enterprise AI features (permissions, auditability, compliance constraints)

• Experience with safety/policy layers (PII handling, prompt injection defenses, sandboxed tool execution)

Why Join Us

• Build core AI capabilities that directly impact users and product strategy

• Work on cutting-edge, real-world agentic systems—focused on applied engineering (no model training required)

• High ownership, fast iteration cycles, and strong cross-functional collaboration

• Competitive compensation and opportunities for rapid advancement

What Your First 90 Days Could Look Like

• tracing + observability

• an offline eval suite with regression gates

• cost/latency targets and monitoring

• documented failure modes and fallback path