AI Engineer at onit

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 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 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) 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 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