Own the technical vision and end-to-end productionization of engineer-focused GenAI tooling and products. Translate ambiguous user problems into shippable solutions; lead multi-team architecture and delivery of core platform capabilities—evaluation pipelines, tracing/observability, dataset/prompt/version workflows, guardrails/safety, and inference orchestration—while setting a high bar for reliability, security, and cost efficiency. Set the standard for technical architecture and implementation in Python, Node/TypeScript APIs, and React UI to deliver end-to-end slices, instrumenting quality/latency/cost/safety and driving continuous improvement against defined SLOs. Lead the technical aspects of the engineering team, mentor engineers, set high standards in architecture and developer-experience, architecture and developer-experience standards, and influence senior leadership across all business functions with clear trade-off narratives and mechanisms that scale.
Job requirements:
- 10+ years software engineering with recent hands-on coding.
- Experience operating at Principal/Lead/ Distinguished Engineer position (or Head of Engineering or Engineering Lead still shipping code).
- Production GenAI ownership (not prototypes): e.g., RAG, eval frameworks, inference services, safety/guardrails, tracing/observability.
- Python expert; practical TypeScript/React/Node to contribute and guide full-stack work.
- Demonstrated facilitative leadership: improved multi-team architecture/decisions; partnered with senior business leaders on product direction.
- Strong cloud/platform chops (AWS/GCP/Azure), containers/K8s, CI/CD, telemetry, scalable data pipelines.
Nice to have:
- Hands-on with RAG evals and infra: Vector databases (Pinecone, Weaviate, Milvus, Qdrant), evaluation frameworks—especially ragas—OpenTelemetry, and model gateways.
- Performance & cost optimization of inference (batching, caching, KV, quantization)
- Blockchain/tamper-evidence concepts—useful but not required.
Job responsibilities:
- Enable GenAI production-readiness (platform, not model R&D): Partner with product squads to take GenAI features from tooling setup through release and iteration—owning the standards, pipelines, and SLOs that make them reliable.
- Review & recommend AI best practices: Evaluate GenAI designs and implementations; explain how/why they work and recommend changes to improve latency, quality, cost, and safety.
- Drive tooling & pipeline decisions: Run hands-on evaluations/POCs and make final calls on data pipelines, eval frameworks, workflow/orchestration tools, guardrails/safety, model/service integrations, and release mechanisms.
- Define how AI features ship to customers: Deliver and maintain a practical playbook for AI releases—readiness criteria, eval gates, safety guardrails, rollout/rollback, and measurement—so customers get predictable quality.
- Demonstrate business impact: Ensure solutions are production-used and mission-critical; define & track KPIs/OKRs (adoption, accuracy, p95/p99, cost per request, safety incidents).
- Partner with leadership on vision: Shape the technical vision for engineer-focused AI tooling; translate ambiguous user problems into shippable, staged work with clear success criteria.
- Influence product direction: Provide trade-off narratives grounded in customer impact, security, and long-term maintainability; propose sequencing and de-risking plans.
- Design, build, and own core components: Eval pipelines, tracing/observability, dataset/prompt/version workflows, guardrails/safety, and inference orchestration—own these as first-class platform capabilities.
- Lead high-impact contributions across the stack: Act as a force multiplier—set patterns, pair, review, and land critical changes in Python services, Node/TypeScript APIs, and React UI to unblock teams and raise the bar. Not expected to own full end-to-end projects solo.
- Raise the bar on architecture and engineering practice: Write clear architectural documentation, lead design reviews, set lightweight engineering principles, and build mechanisms that help teams make good decisions without central bottlenecks.
- Instrument everything: Establish and operate metrics for quality, latency, cost, and safety; drive continuous improvement loops tied to SLOs and error budgets.
- Mentor, give actionable feedback, and grow careers: Coach senior and junior engineers, provide regular, constructive feedback, co-create growth plans, define clear levelling signals, support hiring loops, and scale bar-raising practices across teams.
- Operate with excellence: Set SLAs/SLOs, own on-call/incident posture for GenAI services, and ensure security, compliance, and cost efficiency at scale.
- Advise senior leaders through clear technical documents: Write persuasive technical proposals and briefs that align multi-team initiatives and inform investment choices and risk/mitigation for GenAI programs.
Benefits:
- Fully remote, work from home env
- Flexible working hours
- Paid Time-Off
- Periodic in-person offsites globally (travel permitting)
- Long-term incentive programs
- Continued education support
- Advancement opportunity
