AI Engineer

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• tracing + observability
• an offline eval suite with regression gates
• cost/latency targets and monitoring
• documented failure modes and fallback path

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