Senior AI/ML Engineer

About the Role: 

We're looking for a senior AI engineer who thinks in context strategies, agent architectures,
and quality feedback loops-and can also write solid backend code. This is a production AI
engineering role, not research or data science. You'll own significant parts of our AI engine, ship
agentic workflows end-to-end, and drive measurable quality improvements on real customer
problems.

You'll be autonomous, hold systems end-to-end, and use AI tools as a natural part of your
daily workflow. You'll raise the engineering bar across the team through clean code, systematic
testing, and sharp code reviews.

Responsibilities:

• Own the AI engine: Design and evolve context architectures (templates, few-shot examples,
structured outputs); manage context window limits; optimize for quality and cost; validate
schemas and handle edge cases
• Architect and ship agentic workflows: Design agent boundaries, clean tool interfaces, failure
handling, and human oversight points; manage agent state across turns; ensure robustness
through guardrails and graceful degradation
• Drive AI quality: Define success criteria before shipping; build and run eval sets; catch
regressions before users do; analyze failure patterns systematically; iterate on evidence, not
gut feel
• Own AI production operations: Trace LLM calls and agent steps across the stack; monitor
cost and latency; set SLOs; respond to incidents; establish operational runbooks
• Write solid Python backend code: Build APIs, microservices, and database schemas that
support the above; own deployment and on-call for your services
• Raise the engineering bar: Champion clean code, the testing pyramid, and sharp code reviews
across the team

Must-Have Requirements:
• 3+ years shipping AI/LLM-powered features in production (not research, not prototypes)
• Hands-on context architecture design: Prompt engineering, structured outputs, schema
validation, few-shot design, context window optimization
• Experience building and operating agentic systems: Tool interface design, orchestration
patterns, failure handling, agent state management, multi-turn conversations

• Systematic approach to AI quality: Eval sets, success criteria definition, failure pattern analy-
sis, evidence-based iteration

• Production AI observability: Tracing LLM calls and agent steps, cost monitoring, latency
tracking, incident investigation
• Proficiency in Python (production-grade, enterprise experience)
• Solid backend fundamentals: APIs, microservices, SQL database design and optimization
• Daily hands-on use of AI development tools (Cursor, Claude Code, Copilot, or similar) — this
is a hard requirement
• Fluent English (written and verbal)
• Self-driven, product-minded, no hand-holding needed
Has owned a non-trivial AI feature or agentic workflow in production for 12+ months — context
design, evals, on-call, iteration on real user feedback

What You'll Work On in Your First 3 Months:
• Build and ship a new agentic workflow end-to-end — design, tools, evals, rollout to a real client
• Tackle a class of LLM reliability issues (e.g. streaming timeouts with reasoning models,
gateway fallback edge cases)
• Close observability gaps so a single conversation can be traced cleanly across our stack

Nice to Have:

• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, LangGraph, etc.)
• Multi-agent system design and operation
• Model routing, cost governance, or LLMOps tooling
• Familiarity with evaluation frameworks (LangSmith, RAGAS, custom harnesses)
• Observability tooling (Datadog, Grafana, OpenTelemetry, Langfuse)
• AWS infrastructure experience (Terraform, Ansible)
• Node.js or TypeScript backend experience

Why Join Us:

• Join a small team of passionate engineers dedicated to innovation and excellence
• Work on a product that genuinely improves people's lives and workplace safety
• Experience a startup culture: fast-paced, close collaboration, real influence on key decisions
• Short feedback loops — ship fast, learn fast
• Minimal bureaucracy — focus on what matters: building great software
• AI-first engineering culture — we embrace and invest in AI-augmented development

Interested in joining our team? Send your CV and a brief cover letter.

Please include:
• Your GitHub profile or portfolio (if available)
• A brief note on your experience with AI/LLM tools
• Your availability and preferred start date

We review applications on a rolling basis and aim to respond within 5 business days.