Працював в 3 компаніях 9 років 2 місяці
IT
AI Engineer
Назву компанії приховано
IT
4 роки 4 місяці
08.2021 - 11.2025
- Owned GenAI delivery. From idea to production.
- Built LLM-powered features: chat, summarization, extraction, assistants.
- Implemented RAG over internal data. Grounded answers.
- Built ingestion pipelines: parse > chunk > embed > index.
- Added vector search + hybrid retrieval (keyword + vector).
- Designed tool/function calling. Deterministic workflows.
- Enforced structured outputs (JSON schema). Reduced hallucinations.
- Built evaluation suite: golden tests, prompt regression, failure replay.
- Implemented guardrails: prompt-injection defenses, PII handling, policy checks.
- Optimized cost/latency: caching, batching, model routing, rate limits.
- Deployed services with Docker/K8s. Production SLAs.
- Added observability: traces, metrics, logs for AI flows.
- Led architecture reviews. Mentored engineers. Owned standards.
AI Engineer
Назву компанії приховано
IT
3 роки 4 місяці
04.2018 - 07.2021
- Delivered AI features for multiple client products. Production mindset.
- Built backend APIs for AI workflows. Clean contracts.
- Implemented document intelligence: extraction, classification, normalization.
- Built search assistants: knowledge base Q&A with retrieval + citations.
- Integrated LLM APIs and tool workflows into existing systems.
- Built async processing: queues, workers, retries, idempotency.
- Productionized AI endpoints: auth, rate limits, timeouts, fallbacks.
- Implemented monitoring + alerting for model/API failures.
- Tuned performance: lower response times, stable throughput.
- Supported releases and hotfixes. Calm under pressure.
- Worked cross-team with product/QA/frontend. No integration surprises.
AI Engineer
Назву компанії приховано
IT
1 рік 6 місяців
10.2016 - 03.2018
- Built backend foundations for data-driven features.
- Implemented data pipelines: cleaning, normalization, scheduled processing.
- Built rule-based automation > later upgraded to ML/AI-ready flows.
- Designed reliable storage and APIs for analytics/search.
- Improved stability and release discipline.
- Collaborated daily with QA/design/backend teams. Delivered on time.
Ключова інформація
- GenAI / LLM engineering
- LLM APIs (OpenAI/Claude/Gemini), prompt engineering, tool/function calling
- Structured outputs (JSON schema), deterministic workflows, fallback strategies
- RAG: chunking, embeddings, retrieval, reranking, grounding, citations
- Agents/workflows: multi-step tool use, orchestration, state handling
- Evaluation: golden datasets, prompt regression, quality gates, human review loops
- Safety: prompt-injection defenses, PII handling, access controls, content filtering
- Data & vector search
- ETL/ELT pipelines, data cleaning, document parsing (PDF/HTML/CSV)
- Vector DB/search (pgvector/Pinecone/Weaviate/FAISS), hybrid search patterns
- Indexing strategies, incremental updates, deduplication, versioned corpora
- AI Backend
- API design (REST/gRPC), microservices, async jobs/queues, caching (Redis)
- Reliability: retries, idempotency, circuit breakers, rate limiting
- Security: OAuth2/JWT, RBAC, secrets management, audit logging
- MLOps / LLMOps
- Model serving patterns (vLLM/TGI or managed inference), autoscaling
- CI/CD for AI services, environment parity, safe rollouts, rollback plans
- Observability: OpenTelemetry, structured logs, AI latency/cost dashboards
- Cloud & delivery
- Docker, Kubernetes, GitHub Actions/GitLab CI
- AWS/GCP/Azure basics (compute, storage, secrets, monitoring)
Навчався в 1 закладі
Alte University
Bachelor of Computer Science
2018
Володіє мовами
Англійська
рідна
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Анонімний пошукач
AI Engineer
Київ
Голосіївський район, Дарницький район, Деснянський район, Дніпровський район, Оболонський район, Печерський район, Подільський район, Святошинський район, Солом'янський район, Шевченківський район
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