Full Stack AI Engineer
Data Science UA is a service company with strong data science and AI expertise. Our journey began in 2016 with uniting top AI talents and organizing the first Data Science tech conference in Kyiv. Over the past 9 years, we have diligently fostered one of the largest Data Science & AI communities in Europe.
About the client:
Our client’s mission is to reduce the time and money enterprises spend on state-of-the-art technology by 99%. It is the core constraint that drives the product philosophy. It forces the company to reject incremental improvements and pursue fundamental breakthroughs. These principles are the operating system for how the company thinks, builds, and ships. They are how the company turns radical ambition into reality.
About the role:
We are looking for a Full Stack AI Engineer who will join the team. You’ll be an architect of the revolution against enterprise tech waste. You’ll build the core platform and intelligent agents that power the company’s mission — not optimizing existing workflows, but eliminating them entirely.
Requirements:
– 5+ years of industry experience in product-centric full stack engineering roles;
– Very strong software engineering background with deep expertise in Python/Typescript.
– Hands-on LLM product patterns (prompting, tool use, routing, evals, regression testing);
Experience of working with Agents & LLMs:
– LangGraph/LangChain (know its limitations & trade-offs);
– Multi-provider routing, prompt tooling, trace/eval discipline (LangSmith);
Experience of working with Knowledge & Search Technologies:
– Vector — pgvector on PostgreSQL (semantic);
– Graph traversal — Cypher on Neo4j (relationships & enterprise context graph);
– Keyword/BM25 — lexical precision;
– Cross-Encoder (BGE Reranker) + RRF (blend lists robustly);
Deep knowledge of working with Data & Databases:
– PostgreSQL (Cloud SQL) — primary OLTP;
– SQLAlchemy and BigQuery — analytics/outcomes & cost/waste telemetry;
– Redis (Memorystore) — caching + lightweight queue/broker;
– GCS — object storage (files, audio, artifacts);
Production systems on GCP:
– Experience of working with Docker, Cloud Run (now) → GKE (later) and Pub/Sub;
APIs & integrations:
– Experience of working with FastAPI;
– Slack-native surfaces (human-in-the-loop);
– Enterprise adapters and bespoke integrations (existing MCPs and API platforms insufficient);
Security & Compliance:
– Experience of working with OAuth2/JWT;
– Least-privilege secrets (GCP Secret Manager);
Observability & Reliability:
– Structured logging, Cloud Logging; metrics/alerts → Cloud Monitoring;
– GitHub Actions CI/CD;
– Pre-commit/ruff/pytest, including Async.
Nice to have:
– Experience of working with Frontend: Next.js, Svelte, or SvelteKit;
– Experience of working with voice agents.
– Being observant in working with Datadog, Vertex/Bedrock rails for cost/perf resiliency.
– Previous experience of working in post-training (SFT/RL) or fine-tuning experience applied to real-world KPIs.
Deep knowledge in simulations & workflows:
– SimPy for discrete-event sims;
– Temporal or equivalent for long-running, reliable workflows.
Responsibilities:
Design & Build Autonomous Agents:
– Tackle the end-to-end challenge of creating agents that solve open-ended enterprise problems.
– Wrangling high-volume enterprise data, building robust evaluation frameworks.
– Mastering the interplay between LLM prompting, fine-tuning, and scalable system architecture.
– Lead Customer Conversations & Translate to Product: Work directly with customer executives to translate their most critical objectives into breakthrough product features.
– Build the “New”: Own the most audacious hypotheses, like fully autonomous optimization agents that replace entire consulting engagements and obsolete the need for army-style deployments.
– Master Deep Context: Build systems that understand enterprise architecture patterns better than most architects, automatically discovering waste and optimization opportunities.
– Take single-threaded ownership of core AI product areas, with end-to-end responsibility for customer success and business outcomes.
– Double as a Forward Deployed Engineer: Work regularly onsite with customers, and actually work with the systems.
– Every customer engagement becomes R&D for the platform, turning individual pain points into generalizable breakthroughs that serve the entire customer base.
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