AI / LLM Engineer: рынок

Вакансия

открытая вакансия, прошедшая гейт качества и содержащая не менее двух технологий

Наблюдаемый кандидат

кандидат, чей стек содержит эту технологию; агрегат, без единой записи наружу

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Окно

открытые вакансии на момент сборки снимка

Топ-5: cloud, python, aws, ci/cd, numpy. Python deep (монопольно — async / typing / FastAPI). Каждый provider имеет свои quirks (rate limits + retry strategies + cost per token + features support). LLM Frameworks: LangChain mastery — самый ёмкий job market (LCEL — LangChain Expression Language, callbacks, agents, retrievers), LlamaIndex (RAG-focused — data ingestion + indexing strategies), Haystack (deepset), Semantic Kernel (Microsoft for.NET shops), LangGraph (DAG-style agent orchestration — LangChain), AutoGen + CrewAI (multi-agent — rising), DSPy (Stanford — declarative LLM programming, growing). Vector databases mastery: Pinecone (managed leader), Weaviate (open-source modular), Qdrant (Rust-based — fast self-hosted), Milvus (enterprise scale), Chroma (lightweight dev / prototyping), pgvector (Postgres extension — production-friendly для existing Postgres shops), Elasticsearch dense_vector + Redis Stack (hybrid full-text + vector). Fine-tuning: LoRA + QLoRA через PEFT library (Hugging Face — standard 2026), Unsloth (faster LoRA training), Axolotl (config-driven fine-tuning). Full fine-tuning rare (cost prohibitive). RLHF / DPO / KTO: TRL library (Hugging Face — Direct Preference Optimization рост 2024+). Evaluation: RAGAS (RAG-specific — faithfulness + answer relevancy + context precision), DeepEval, lm-evaluation-harness (academic benchmarks), Inspect AI (UK AISI safety-focused), Promptfoo (regression testing для prompts), LangSmith (LangChain trace + eval — best UX). Observability + tracing: Helicone (LLM-specific APM), LangSmith, Langfuse (open-source), Arize Phoenix, Datadog LLM Observability (2024+ feature). Cost optimization: prompt caching, prompt compression (LLMLingua), model routing (small model for easy queries, large для complex — through providers like Martian / OpenRouter).

Data Scientist — focus statistical modeling + A/B testing + business problem framing. Стек: pandas + sklearn + XGBoost + Jupyter + SQL + Tableau. См. Data Scientist. ML Engineer — focus на production ML pipelines + deployment + scaling. Стек: PyTorch + sklearn + Kubernetes + MLflow + cloud-managed ML. Универсал — работает с recsys / fraud / ranking / classical ML. См. ML Engineer. AI Engineer (эта страница) — focus на LLM integration в продукт. НЕ обучает foundation модели (это Research), НЕ строит classical ML pipelines (это ML Engineer). Стек: Python + LLM providers + LangChain / LlamaIndex + vector DBs + vLLM serving + RAG patterns + LoRA fine-tuning + LLM evaluation tools. Key distinction 2026: AI Engineer reuses pre-trained LLM, ML Engineer часто trains models из scratch. AI Engineer работает с API/prompts/RAG; ML Engineer — с training loops + feature engineering. Career-pivots: Backend Senior → AI Engineer Junior — 2-4 месяца (быстрая ramp за счёт API-based work + LangChain learnable). ML Engineer Senior → AI Engineer Senior — 1-3 месяца (already знает stack basics, нужно deepen LLM-specific). DS Senior → AI Engineer Middle — 3-6 месяцев. AI Engineer Senior → Research — требует deeper math + paper-writing experience + 6-12 месяцев preparation.

Decision tree для LLM customization 2026: 1) Prompt Engineering — first try. Use case: zero-shot или few-shot ENOUGH для task. Pros: zero-cost (no training), instant iteration, model-agnostic. Cons: limited control, prompt fragility, context window pressure. 2) RAG (Retrieval-Augmented Generation) — best для knowledge-grounded tasks. Use case: chatbot, Q&A, document search, customer support, code documentation, internal knowledge bases. Architecture: query → embed → vector search → top-k documents → augment prompt с retrieved context → LLM generates answer. Pros: factual grounding (anti-hallucination), up-to-date (just update index, no retraining), source citation possible, cheaper чем fine-tuning. Cons: retrieval quality bottleneck, prompt size grows. RAG stack 2026: ingestion (LlamaIndex / LangChain document loaders) → chunking strategy (semantic vs fixed-size vs recursive) → embeddings → vector DB (Pinecone / Qdrant / Weaviate / pgvector) → retrieval (similarity + reranking via Cohere Rerank / BGE Reranker) → prompt augmentation → LLM generation. Advanced RAG: hybrid search (vector + BM25), HyDE (Hypothetical Document Embeddings), query rewriting, multi-hop retrieval, self-reflection (CRAG / Self-RAG). 3) Fine-tuning — use when prompt engineering + RAG не достаточно. Use case: a) domain-specific style / format (medical / legal — specific phrasing patterns), b) reduce token cost (smaller fine-tuned model заменяет larger general model), c) tasks requiring deep specialized knowledge (где RAG context window не помогает). Modern fine-tuning: LoRA / QLoRA (PEFT) — train small adapter (~1% of weights), full fine-tuning rare. Stack: Hugging Face Transformers + PEFT + TRL + Unsloth (faster) + Axolotl (config-driven). Hardware: 24GB GPU (RTX 4090 / A6000) для 7-13B models, 80GB+ (A100/H100) для 70B+. Cost reality 2026: prompt engineering — minutes, $0. RAG setup — 1-2 weeks engineering, $100–1000/month API costs. 4) Combination: real production AI features typically use ALL three. Prompt engineering первое (system prompts + few-shot examples) → RAG для grounding → optional LoRA fine-tuning для style/format. 5) Agentic patterns (rising 2025–2026) — LLM + tool use + multi-step reasoning. Use LangGraph / AutoGen / CrewAI / DSPy. Default 2026: RAG для 80% production use cases + prompt engineering для prototyping + LoRA fine-tuning только если RAG explicitly insufficient + agents для complex multi-step workflows.

Да, 0% AI Engineer-вакансий — full-remote или гибрид. AI Engineer work fully cloud-based (LLM APIs + vector DBs + cloud GPUs — все remote-accessible). Аутсорсеры (EPAM AI / Luxoft AI / Andersen AI / DataArt AI) — почти всегда remote на US-AI-projects. Российские продуктовые (Сбер.AI / Яндекс AI / Тинькофф AI / Ozon AI) — гибрид или remote после probation. Российские банки — гибрид/офис AI-data compliance. Международные frontier-AI labs — full-remote standard для exceptional candidates. Big Tech AI — гибрид-standard. Y Combinator AI-startups — premium full-remote. Английский для international AI-remote — must (премия +40-60%, и AI community / papers / docs полностью англоязычные).

Roadmap: 1) Python deep + Backend basics — async / typing / FastAPI / pytest. Без Backend skill — не AI Engineer (just prompt-jockey). Книга: «Fluent Python» Ramalho. 2) LLM fundamentals understanding — transformer architecture basics (attention + tokenization + decoding strategies + context windows), но НЕ нужно implement from scratch. Курс: «Building LLMs from scratch» Karpathy YouTube (free — best для intuition). Книга: «Hands-On Large Language Models» Jay Alammar / Maarten Grootendorst (2024 — must-read). 3) OpenAI API mastery — chat completions + function calling + structured outputs + streaming + assistants. Build minimum 3 real apps. Free credits + cheap experimentation. 4) Anthropic Claude API mastery — лучший для coding + long-context use cases. Tool use + prompt caching + computer use (если AI Agent track интересен). 5) LangChain mastery — самый ёмкий job market требование. LCEL (LangChain Expression Language), chains, agents, retrievers, callbacks. Курс: «LangChain & Vector Databases in Production» DeepLearning.AI (free). 6) RAG architecture deep — document loaders + chunking strategies (semantic / fixed / recursive) + embeddings choice + vector DB selection + retrieval algorithms + reranking. Build production RAG app с advanced patterns (hybrid search + reranking + HyDE). 7) Vector DB mastery — minimum один deeply (Qdrant — best для self-hosted learning) + один managed (Pinecone). Understanding HNSW / IVF / PQ algorithms basics. 8) Fine-tuning hands-on — LoRA / QLoRA через PEFT library + Unsloth (faster). Fine-tune Llama 3.x 8B на own dataset. Hardware: Google Colab Pro+ ($50/month) или RunPod / Vast.ai (cheap GPU rental). 9) vLLM serving — deploy open model with vLLM + benchmark throughput + compare with OpenAI API. 10) Evaluation mastery — RAGAS + DeepEval + custom evaluation suite. Build regression test для prompts. 11) LangSmith / Langfuse observability — instrument production app + analyze traces + identify bottlenecks. 12) Agent frameworks — LangGraph + AutoGen + CrewAI + DSPy basics. Build multi-agent demo (research agent + writing agent + critic agent). 13) Pet-проект portfolio: production-grade AI feature (e.g. document Q&A для open dataset) с RAG + evaluation + observability + cost tracking. Document как portfolio (GitHub + blog post + demo video). Курсы РФ: Karpov.Courses «AI Engineer» track, Otus «LLM Engineering», Школа высшая ВШЭ ИИ, School21 (Сбер) AI Engineer track. Books-must: «AI Engineering» Chip Huyen (2024), «Hands-On Large Language Models» Alammar / Grootendorst, «Building LLMs for Production» Bouchard / Peng. Backend Senior + LLM interest → AI Engineer Junior — 2-4 месяца (fastest entry в ML world). ML Engineer Senior → AI Engineer Senior — 1-3 месяца. Data Scientist → AI Engineer Middle — 3-6 месяцев.

10 активных открытых AI Engineer-вакансий в нашей выборке — молодая ниша (выделилась в 2023 после ChatGPT boom). Реальный рынок гораздо шире — большинство AI Engineer roles classified нашим matcher'ом как general ML Engineer или Backend (потому что titles часто «Senior Backend Engineer (AI focus)» или «ML Engineer — LLM»). Истинный пул AI-focused dev jobs в СНГ + Европе оценочно 500–2000 позиций активных любой момент 2026 (с учётом нечётко классифицированных). География: 🇷🇺 Россия / 🇵🇱 Польша / remote. Время закрытия Senior AI Engineer — 4-10 недель в РФ, 6-12 недель international (rare-skill premium). Trend 2026: AI Engineer пул растёт fastest среди всех IT-категорий — мы видим 3-5× year-over-year growth в LLM-mentioning job postings.

Senior AI Engineer владеет полным циклом LLM-product engineering + cost optimization + technical leadership. Python deep + Backend Senior level: async / typing / FastAPI / pytest mastery. Open LLM knowledge: Llama 3.x / Mistral / Qwen 2.5 / DeepSeek V3 + R1 / Phi 3 / Gemma 2 — performance / cost trade-offs, когда какую использовать. LangChain mastery: LCEL deep, callbacks, agents, retrievers, custom chain authoring, debugging through LangSmith. Or equivalent в LlamaIndex / Haystack / Semantic Kernel. RAG architecture mastery: advanced patterns (hybrid search BM25 + vector, reranking, HyDE, query rewriting, multi-hop retrieval, self-reflection patterns CRAG / Self-RAG), chunking strategies (semantic + recursive + hierarchical), embedding model selection. Vector DB mastery deep: один из Pinecone / Qdrant / Weaviate / Milvus / pgvector deeply — index tuning, sharding, replication, hybrid search implementation. Fine-tuning mastery: LoRA / QLoRA via PEFT + Unsloth + Axolotl, dataset preparation (instruction tuning + DPO data), hyperparameter selection, evaluation methodology. RLHF / DPO / KTO basics: TRL library для preference optimization. Serving mastery: vLLM advanced (PagedAttention + continuous batching + speculative decoding + KV cache optimization), TGI, SGLang. Agent architectures: LangGraph + AutoGen + CrewAI + DSPy mastery, MCP (Model Context Protocol) implementation, tool use design patterns, multi-agent orchestration. Evaluation mastery: RAGAS + DeepEval + custom evaluation suite design, LLM-as-judge methodology, regression testing для prompts, A/B testing для LLM features. Observability + tracing: LangSmith + Langfuse + Helicone + Arize Phoenix + Datadog LLM Observability mastery. Cost optimization mastery: prompt caching strategies (native + custom Redis), prompt compression (LLMLingua), model routing (small for easy / large для complex via OpenRouter / Martian), batch API for non-real-time. Typical Senior mandate: 50-70% reduction в LLM API costs. Safety / guardrails: NeMo Guardrails, Llama Guard, OpenAI Moderation API integration, prompt injection defense, output filtering. System design для LLM products: design RAG-based product на whiteboard под scale 100M+ queries/day, multi-region considerations, latency budgets (target P95 < 2s for chat). Soft: ADRs writing для LLM architecture decisions, technical writing (LLM feature design docs), cross-team collaboration (Product / Backend / DS / Research teams), mentoring Middle AI Engineers, paper-reading discipline (NeurIPS / ICLR / EMNLP — must follow). Английский для Senior+ MUST — AI community / docs / papers / conferences полностью англоязычные. Optional bonus: open-source contributions в LangChain / LlamaIndex / vLLM / Hugging Face — резко повышают market value для frontier-AI labs hiring. Twitter AI presence (regular posting about LLM experiments) — important для hiring through community connections.