MLOps Engineer: рынок

Вакансия

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

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открытые вакансии на момент сборки снимка

Топ-5: mlops, ci/cd, aws, cloud, python. Python (основной — для custom infrastructure), Go bonus (для performance-critical infra components — например serving proxies). Docker mastery (multi-stage builds для optimized ML images, GPU containers). Kubernetes mastery (production standard для ML serving — Deployment / StatefulSet / DaemonSet / Job / CronJob, RBAC, Network Policies, Resource Quotas, HPA / VPA / Cluster Autoscaler). GPU operator (NVIDIA GPU Operator для K8s GPU scheduling). K8s operators для ML: Kubeflow Training Operator + Spark Operator + Argo Workflows. Helm + Kustomize + ArgoCD / FluxCD — GitOps standard. Terraform / Pulumi / CloudFormation (IaC для cloud-managed ML — AWS / GCP / Azure resources). MLflow mastery (open-source standard для model registry + tracking — Senior владеет MLflow Plugins customization). Kubeflow + Kubeflow Pipelines (K8s-native ML orchestration — крупные enterprise часто требуют). Workflow orchestrators: Airflow + Prefect + Dagster (один deeply — Dagster rising 2026). Feature stores: Feast (open-source) или Tecton (managed) — must для Senior MLOps. Model serving: Triton Inference Server (NVIDIA — multi-framework + dynamic batching) + BentoML (Python-friendly) + TorchServe + KServe (K8s-native serving). LLM-specific serving: vLLM (PagedAttention — рекорд для high-throughput LLM serving), Text Generation Inference (TGI) от Hugging Face, SGLang (programming model для LLM serving + caching), Ollama (local LLM serving). Observability: Prometheus + Grafana + Loki + OpenTelemetry. ML monitoring + drift detection: Evidently AI + WhyLabs + Arize AI + Fiddler (один deeply). Data version control: DVC + LakeFS. Cloud-managed ML: один из AWS SageMaker / GCP Vertex AI / Azure ML / Databricks deeply. Distributed compute platforms: Spark + Ray mastery. Cost optimization: spot instances strategy, GPU sharing (NVIDIA MIG), quantization integration, caching strategies (Redis для inference results), batch vs real-time inference cost analysis.

DevOps Engineer — фокус на general application infrastructure (CI/CD + monitoring + cloud + Kubernetes + IaC). Стек: Docker / K8s / Terraform / Prometheus / Grafana / Ansible / Jenkins / GitHub Actions / cloud (AWS / GCP / Azure). См. DevOps Engineer (general) (когда страница ship'нется). Platform Engineer — DevOps + internal-tooling focus. Строит developer-platform для разработчиков (К8s clusters management + ArgoCD + IDP — Internal Developer Platform — Backstage / Port / Cortex). MLOps Engineer (эта страница) — DevOps + ML-specific focus. Строит ML-platform для DS / ML Engineer / Research teams. Дополнительно к DevOps stack: MLflow / Kubeflow / Feature Stores / Model Serving (Triton / vLLM) / ML Monitoring (Evidently / WhyLabs) / cloud-managed ML (SageMaker / Vertex AI). ML Platform Engineer — Senior MLOps с focus на reusable platform-as-product (для всей organization, не single team). Career-overlap: DevOps Middle → MLOps Junior — за 6-12 месяцев (learn ML stack + Kubeflow / MLflow + cloud-managed ML). MLOps Senior ↔ Platform Engineer Senior — easy lateral move (much shared stack). ML Engineer Middle → MLOps Junior — за 4-8 месяцев (already knows ML stack, need to deepen Kubernetes + Terraform + observability). DevOps → MLOps — более distant чем Platform Engineer pivot (ML knowledge gap).

Decision tree для ML platform choice 2026: 1) MLflow (open-source, vendor-neutral) — best для experiment tracking + model registry + simple deployment. Lightweight. Self-hosted или managed (Databricks). Use case: основа ML platform для большинства организаций. Default 2026 для team-level ML tracking. 2) Kubeflow + Kubeflow Pipelines (open-source, Kubernetes-native) — best для enterprise K8s-first организаций которые хотят полный ML-platform stack без vendor lock. Heavier setup. Includes: Pipelines (workflow) + Training Operator (distributed training) + Serving (KFServing → KServe) + Notebooks. Use case: large enterprise + Kubernetes-mature team + multi-cloud / on-prem requirement. 3) AWS SageMaker (managed) — best для AWS-native организаций. Comprehensive: SageMaker Studio (IDE) + Training Jobs + Hyperparameter Tuning + Pipelines + Endpoints (real-time + batch) + Model Registry + Feature Store + Clarify (bias detection). Vendor lock to AWS. Use case: AWS-first organization + small ML team — выгодно за counts of features в one platform. Cost-heavy at scale (expensive). 4) GCP Vertex AI (managed) — Google's SageMaker equivalent. Strong на: Vertex AI Workbench (Jupyter cloud), Pipelines (Kubeflow-based — portable), AutoML, TPU access (для research / large training), Model Garden (pre-trained models). Use case: GCP-first organization + Kubeflow-portability важна. 5) Azure ML (managed) — Azure equivalent. Strong on: Designer (visual ML pipeline builder), AutoML, MLOps capabilities, Azure-native integration. Use case: Azure / Microsoft-first organization. 6) Databricks (managed multi-cloud) — best для Spark-first ML workloads + Lakehouse. MLflow создатели. Premium pricing. Use case: data-heavy организации с Spark / Lakehouse pattern. 7) Specialized platforms: Weights & Biases (best UX для experiment tracking — research-favorite), Comet (W&B alternative), Neptune, Determined AI (distributed training platform), Run:AI (GPU orchestration — acquired by NVIDIA), Truefoundry + Bento (modern model serving). Default 2026 для new ML team: MLflow self-hosted (или Databricks managed if Spark-heavy) + Kubernetes serving (Triton / vLLM) + Feast (feature store) + Prometheus + Grafana + Evidently AI. Default для AWS-native enterprise: SageMaker (если cost-acceptable + small team) или MLflow on EKS + custom services (cheaper at scale). Default для Kubernetes-mature enterprise: Kubeflow + MLflow.

Да, 35% MLOps-вакансий — full-remote или гибрид. MLOps work cloud-based (вся infrastructure в cloud K8s / managed services). Аутсорсеры (EPAM AI Platform / Luxoft / Andersen) — почти всегда remote на US-AI-projects. Российские продуктовые — гибрид или remote после probation. Российские банки (Сбер AI banking / ВТБ) — гибрид/офис из-за security compliance. Международные ML-platform-companies (Truefoundry / Bento / Weights & Biases / Comet / Domino Data Lab / Determined AI / Run:AI) — full-remote standard. Big Tech ML Platform — гибрид-standard, частично remote. Релокант-хабы для MLOps: Польша (Варшава / Краков — AI Platform-friendly), Германия (Berlin / Munich), Канада (Toronto), Сербия, Грузия, ОАЭ. Английский для international MLOps-remote — must (premium +30-50%, и MLOps интенсивно cross-team collaboration English-speaking).

MLOps Engineer (general) — может работать в любой ML-team (product team / research lab / AI consultancy). Стек универсальный MLOps. ML Platform Engineer — Senior MLOps с focus на reusable platform-as-product для всей organization (не для одной team). Дополнительно: internal API design (для других ML Engineer / DS), self-service tools (templates / scaffolding / CLI tools — типа generate-new-model-project), SLA management для platform components (uptime / latency / throughput targets), cost-chargeback systems (per-team compute usage tracking + billing), onboarding documentation + training programs для other ML team members, backwards-compatibility management для shared platform components, open-source contribution в platform components (e.g. Feast / Kubeflow contributions). Зарплата ML Platform Engineer на 15-25% выше Senior MLOps. Staff / Principal MLOps — еще выше, типично managing ML platform team (3-7 MLOps engineers + integrations с DevOps / SRE / Data Platform leads). Career-выбор: MLOps Engineer если нравится hands-on каждый день в новой problem, ML Platform Engineer если нравится building reusable systems и working с multiple consumer-teams. Все три legitimate Senior+ paths.

Roadmap: 1) Strong DevOps base — Linux + networking + bash mastery, Docker mastery (multi-stage builds + layer optimization + security scanning), Kubernetes mastery deep (Deployment / Service / Ingress / PVC / RBAC / Network Policies / HPA + custom CRDs basics). Книги: «Kubernetes in Action» Lukša, «The Kubernetes Book» Poulton. 2) IaC mastery: Terraform deep (modules / state management / workspaces) + understanding cloud-provider-specific patterns. Курс HashiCorp Learn (free). GitOps patterns с ArgoCD / FluxCD. 4) Cloud platform deep — выбрать один из AWS / GCP / Azure и пройти Solutions Architect Associate / Professional cert. 5) ML basics — minimum: Python + pandas + scikit-learn + PyTorch (basics) + понимать training / inference loops. Курс fast.ai «Practical Deep Learning» Part 1. «Designing Machine Learning Systems» Chip Huyen — MLOps-must-read 2026 (для understanding ML workflow от training до deployment). 6) Experiment tracking + model registry: MLflow mastery deep (Tracking + Registry + Projects + Models). Setup self-hosted MLflow on EKS / GKE / AKS. 7) ML serving stack: Triton Inference Server (multi-framework + dynamic batching) + BentoML + KServe. LLM serving: vLLM mastery (PagedAttention + continuous batching) + TGI + SGLang. 8) Feature Store: Feast (open-source) — setup end-to-end на тестовом проекте (offline + online + retrieval). 9) Orchestration: один из Airflow / Prefect / Dagster + Kubeflow Pipelines basics. 10) ML monitoring: Evidently AI (drift detection + data quality) + WhyLabs или Arize AI или Fiddler — один deeply. 11) Pet-проект: full ML platform deployment (K8s cluster + MLflow + Feast + Triton + Prometheus + Grafana + Evidently AI) с simple model lifecycle (train → register → deploy → monitor → retrain). Документируйте в GitHub README. 12) Open-source contribution — Feast / Kubeflow / MLflow / vLLM / Triton — резко повышает MLOps-resume value. Курсы РФ: Karpov.Courses «MLOps», Otus «MLOps», SkillFactory MLOps track, Slurm MLOps интенсив. International (eng): «Made with ML» MLOps course Goku Mohandas (free, comprehensive), Hugging Face MLOps course, Coursera «MLOps Specialization» DeepLearning.AI, Databricks Academy MLOps. Books-must: «Designing Machine Learning Systems» Chip Huyen, «Machine Learning Design Patterns» Lakshmanan, «Reliable Machine Learning» Cathy Chen. DevOps Middle / ML Engineer Middle → Junior MLOps — 4-10 месяцев. Backend Senior → Junior MLOps — 8-14 месяцев (need to learn ML stack).

188 активных открытых MLOps-вакансий — растущая ML-infrastructure ниша. География: 🇷🇺 Россия / 🇵🇱 Польша / remote. Реальный рынок шире за счёт международного remote-сегмента (ML-platform-companies — Truefoundry / Bento / W&B / Comet / Determined AI / Domino Data Lab + AI-startup MLOps teams — full-remote standard). Время закрытия Senior MLOps — 5-10 недель (faster чем Research, slower чем DevOps general за счёт rare ML-skill combination). MLOps — самая «recession-resistant» AI/ML ниша (компании сокращают research first, MLOps continuously needed для production).

Senior MLOps владеет полным циклом ML-platform engineering + technical leadership. Programming mastery: Python deep + Go bonus для performance-critical infra. Docker mastery: multi-stage builds, layer optimization, GPU containers, security scanning (Trivy / Snyk). Kubernetes mastery deep: Deployment / Service / Ingress / PVC / RBAC / Network Policies / HPA / VPA / Cluster Autoscaler, custom CRDs basics, GPU Operator + Kubeflow Training Operator + Spark Operator + Argo Workflows. IaC mastery: Terraform deep (modules / state / workspaces), один из Pulumi / CloudFormation bonus. GitOps mastery: ArgoCD / FluxCD + Kustomize / Helm patterns. Cloud platform deep: один из AWS / GCP / Azure на уровне Solutions Architect Professional. MLflow mastery: deep customization + Plugins + multi-tenancy patterns. Kubeflow Pipelines: enterprise-scale deployment + custom components + multi-tenancy. Feature Store mastery: Feast deep (offline + online + retrieval + monitoring + multi-tenancy) или Tecton (managed). Model serving mastery: Triton Inference Server deep (ensembles + dynamic batching + model repositories) + BentoML + KServe. LLM serving mastery (2026 must): vLLM deep (PagedAttention + continuous batching + speculative decoding + KV cache optimization) + TGI + SGLang + Ollama. Distributed compute platforms: Spark + Ray mastery (Spark Operator on K8s, Ray Train + Ray Serve). Observability deep: Prometheus + Grafana + Loki + OpenTelemetry + custom metrics + alerting strategies. ML monitoring: Evidently AI + WhyLabs / Arize AI / Fiddler integration patterns. Cost optimization mastery: spot instances strategy + GPU sharing (NVIDIA MIG) + quantization integration (INT8 / FP16 / FP4) + caching strategies (Redis для inference results) + batch vs real-time inference cost analysis + reserved instance planning. Typical Senior MLOps mandate: 30-50% reduction в compute costs через combination этих techniques. Security mastery: secrets management (Vault / cloud KMS), network policies, supply chain security (signed images), data privacy (PII handling in ML pipelines). System design для ML platforms: дизайнить ML platforms на whiteboard под scale 100M+ predictions/day + multi-tenant + multi-region. Soft: ADRs writing для platform decisions, technical writing (platform documentation + onboarding materials), code-review для multiple teams, mentoring Middle MLOps, cross-team collaboration (DevOps / SRE / Data Platform / ML Engineer / DS / Research teams). Английский для Senior+ MUST — MLOps интенсивно cross-team English-speaking. Optional bonus: open-source contributions в Feast / Kubeflow / MLflow / vLLM / Triton — резко повышают market value для frontier-AI labs + ML-platform-companies.