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
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
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
Core Strengths (Required)
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)
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
Ship one production agent workflow end-to-end with:
tracing + observability
an offline eval suite with regression gates
cost/latency targets and monitoring
documented failure modes and fallback path
