Full Stack AI Engineer @ Data Science UA

13 hours ago

Remote Kiev, Czech Republic Data Science UA Full time

- 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. ,[ 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] Requirements: Python, Full Stack, TypeScript, LLM, LangChain, LangGraph, PostgreSQL, BigQuery, Redis, GCS, GCP, Docker, FastAPI, CI/CD, VertexAI, Bedrock, SimPy, Next.js, Datadog

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