Data Science UA is a service company with deep expertise in AI and Data Science. Our story started in 2016 with the first Data Science UA Conference in Kyiv, and since then, we’ve built one of the largest AI communities in Europe.

We are looking for a Head of Engineering for our client’s team, which is building the AI-native distribution layer for insurance.
This is a builder-leader role. You are responsible for shipping the actual product: you own engineering execution, most of the architecture, the quality bar, and the team. The CTO sets product direction and priorities and stays close to the hardest technical problems; you own turning that into shipped, reliable software with a team you build and grow. If you want to own building a sophisticated AI product end-to-end, it is your seat.

– Building the product. You own the engineering output – architecture, technical decisions, and shipping. Hands-on enough to design the hard parts and review the critical code, not a hands-off manager.
– The team. Manage, coach, hire, and grow engineers, organized into focused pods with tech leads (Voice/AI, Campaigns + Integrations, Platform/Reliability).
– Reliability & quality — our #1 lever. You own the release gate, on-call, incident response, and driving reliability from “firefighting” to “boring.” This is the outcome we judge first.
– Velocity through AI-native engineering. We run an AI-first engineering org. You and the team ship with Claude Code and Codex every day – orchestrating coding agents, not hand-typing everything – and you raise the whole team’s leverage with them while never letting speed erode the reliability bar.
– Execution partnership with the Product Designer (peer under the CTO) to scope, sequence, and ship.

– People management is non-negotiable. 3+ years directly leading engineers as an EM / Head of Eng / Director – hiring, growing, retaining, and building pod/tech-lead structure as a team scale. We will dig into this hard.
– 8+ years building production software, and still hands-on – you architect and build the hard parts, not just review.
– You live in Claude Code / Codex. Daily, fluent, opinionated about AI-assisted engineering. In 2026 we consider this table stakes for an eng leader; if you’re not orchestrating coding agents as a core part of how you and your team ship, this isn’t the right fit.
– Owned reliability for a system real customers depend on – on-call, incidents, SLOs, test automation – and made something flaky boring.
– Strong in our domain of engineering: TypeScript/Node, distributed/event-driven systems, a workflow engine (we use Temporal), PostgreSQL, AWS, multi-tenant architecture. Bonus weight for real-time voice/telephony and production LLM/agent systems.
– High-load / distributed systems. You’ve designed and operated systems at real scale and have strong, specific opinions: high-throughput event-driven and streaming architectures, queues and backpressure, idempotency, horizontal scaling of stateful real-time workloads, latency budgets, and capacity planning. You think clearly about how services should communicate and what to reach for, and why. We’re a real-time voice platform with growing call volume and millions of traces.
– Can architect at the infrastructure level. You don’t run DevOps day-to-day (we have a DevOps engineer for that), but you design at that level and make the calls: autoscaling strategy (e.g. HPA vs KEDA), container orchestration, cost/performance trade-offs, observability at scale. You own the infra architecture direction and partner with DevOps on execution.
– Thrives under a hands-on technical founder – you’ve done it, or can clearly say why it energizes rather than frustrates you. The CTO will be in the code on the hard problems; the right person treats that as a multiplier.

Nice-to-Have:

– Real-time voice / telephony (Twilio, SIP, ASR/TTS, latency-sensitive audio).
– Building agentic systems – tools/skills frameworks, MCP, evals, guardrails – in production.
– Applied LLM work: fine-tuning, distillation, eval-driven development.
– Insurance, fintech, or another regulated/compliance-heavy vertical.

Tech stack & tools

– Backend: TypeScript, Node, NestJS · monorepo (libs + apps)
– Orchestration: Temporal (Temporal Cloud) for the campaign/workflow engine
– Data: PostgreSQL on AWS RDS (multi-tenant, row-level security)
– AI: Anthropic + OpenAI models, prompt/agent orchestration, evals; fine-tuned/distilled models on our own data
– Frontend: React / Next.js
– Platform: AWS · Clerk (auth) · Datadog (observability) · Pylon + HubSpot (CS/CRM)
– How we ship: Linear (with agent delegation), GitHub, and Claude Code + Codex as part of daily engineering