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Dvc Jobs (NOW HIRING)

HeadHR

NY · On-site

$60 - $80/hr

Implementacja potoków (pipelines) CI/CD dla rozwiązań ML, obejmujących automatyczne testowanie, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training (CT). * Konteneryzacja i ...

Torch.AI

AI/ML Engineer II

Mclean, VA · On-site

$125 - $150/hr

Familiarity with MLflow, DVC, Kubeflow, SageMaker, or similar tooling. * Experience with graph‑based retrieval, agentic systems, or tool‑use architectures. * Experience supporting defense ...

Torch.AI

AI/ML Engineer II

Leawood, KS · On-site

$125 - $150/hr

Familiarity with MLflow, DVC, Kubeflow, SageMaker, or similar tooling. * Experience with graph‑based retrieval, agentic systems, or tool‑use architectures. * Experience supporting defense ...

Norbert Health

Applied AI Engineer

Brooklyn, NY · On-site

... MLflow, DVC, etc.) • Active learning or human-in-the-loop labeling workflows • C++ for integrating with our computer vision pipeline Company : Norbert Health develops a home medical pod that ...

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Dvc information

See salary details

$70.5K

$86.3K

$106.5K

How much do dvc jobs pay per year?

As of Sep 8, 2026, the average yearly pay for dvc in the United States is $86,295.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,000.00 and $90,000.00 per year, depending on experience, location, and employer.

What is a dvc?

DVCs, or Data Version Control specialists, are professionals who manage and oversee data versioning using tools like Data Version Control (DVC). They help teams track changes to datasets and machine learning models, ensuring reproducibility and collaboration in data science projects. DVCs work closely with data scientists and engineers to implement data pipelines, handle large datasets, and maintain consistency across multiple experiments. Their expertise is essential in organizations where managing complex data workflows is critical for research and deployment.

What are the key skills and qualifications needed to thrive as a dvc?

To thrive as a Divisional Vice Chancellor, you need strong leadership abilities, a background in academia or administration, and typically an advanced degree such as a Ph.D. or Ed.D. Familiarity with university management systems, accreditation processes, and data analysis tools is often required. Exceptional communication, strategic vision, and conflict resolution skills help foster academic excellence and organizational growth. These competencies are critical for effective governance, stakeholder engagement, and advancing the institution’s mission.

How does a dvc engineer typically collaborate with data scientists and machine learning engineers on a project?

A DVC Engineer works closely with data scientists and machine learning engineers to streamline data management, versioning, and reproducibility throughout the machine learning workflow. They help set up and maintain DVC pipelines, ensuring data and model versions are tracked across experiments. Regular communication is essential, as DVC Engineers provide support for integrating DVC into existing workflows, troubleshoot issues, and train team members on best practices. This collaborative effort helps teams maintain consistency, improve efficiency, and facilitate smoother handoffs between stages of model development.

What is the difference between Dvc vs Video Editor?

AspectDvcVideo Editor
Required CredentialsHigh school diploma or equivalent; technical training or certification often preferredHigh school diploma; often a degree or certification in film, media, or related field
Work EnvironmentFilm sets, production companies, or broadcast stationsPost-production studios, editing suites, or freelance work
Industry UsageUsed in film, TV, and video production for technical supportUsed in editing and post-production to craft the final video content

While both Dvc and Video Editor work within the video production industry, Dvc primarily handles technical aspects during filming, such as camera operation and equipment setup. In contrast, Video Editors focus on editing footage to create the final product. Understanding these roles helps clarify career paths and job expectations in video production.

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What states have the most Dvc jobs?

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Dvc jobs near you
Infographic showing various Dvc job openings in the United States as of September 2026, with employment types broken down into 87% Full Time, 4% Part Time, 3% Temporary, and 6% Contract. Highlights an 83% Physical, 2% Hybrid, and 15% Remote job distribution, with an average salary of $86,295 per year, or $41.5 per hour.

MLOps Engineer

NY • On-site

HeadHR
See more jobs at HeadHRexternal

$60 - $80/hr

Other

Posted 7 days ago

Job description

Zakres obowiązków:
  • Tworzenie i utrzymanie platformy AI w branży ubezpieczeniowej:
  • Projektowanie i budowa infrastruktury MLOps/LLMOps: Tworzenie skalowalnego środowiska do trenowania i serwowania modeli przy użyciu Azure Machine Learning, Azure AI Foundry lub Kubernetes (AKS).
  • Automatyzacja procesów CI/CD/CT: Implementacja potoków (pipelines) CI/CD dla rozwiązań ML, obejmujących automatyczne testowanie, wersjonowanie danych i modeli (DVC, MLflow) oraz Continuous Training (CT).
  • Konteneryzacja i orkiestracja: Przygotowywanie obrazów Docker dla modeli AI/GenAI oraz zarządzanie ich wdrożeniami na klastrach Kubernetes w architekturze hybrydowej (integracja z systemami on‑premise).
  • Monitoring i Observability: Wdrożenie zaawansowanego monitoringu modeli (wykrywanie Data Drift/Model Drift), logowania i alertowania.
  • Wsparcie techniczne dla AI Act: Implementacja narzędzi do audytowalności modeli, lineage (śledzenie pochodzenia danych) oraz bezpieczeństwa (zarządzanie dostępem, szyfrowanie) zgodnie z wymogami regulacyjnymi.
  • Optymalizacja kosztów i wydajności: Zarządzanie zasobami chmurowymi Azure, optymalizacja czasu inferencji modeli oraz skalowanie infrastruktury w zależności od obciążenia.

Lokalizacja: Warszawa/Wola; praca hybrydowa – 1x w tygodniu w biurze

Kogo poszukujemy? Wymagania:
  • Minimum 3 lata doświadczenia w obszarze DevOps, MLOps lub Inżynierii Oprogramowania, w tym praktyka w pracy z modelami ML na produkcji.
  • Zaawansowana znajomość Docker i Kubernetes (zarządzanie klastrami, Helm charts, Ingress).
  • Głęboka znajomość Azure (w szczególności Azure ML, AKS, Azure Container Registry) lub GCP/AWS z gotowością do szybkiego wejścia w Azure.
  • Doświadczenie w budowaniu pipeline'ów (Azure DevOps, GitHub Actions, Jenkins) uwzględniających specyfikę ML (np. trenowanie modelu jako krok w pipeline).
  • Dobra znajomość Python (niezbędna do pracy z SDK narzędzi ML) oraz Bash/Shell.
  • Praktyczna obsługa MLflow, Kubeflow lub rozwiązań natywnych chmury do zarządzania cyklem życia modelu.
  • Znajomość Terraform, Bicep lub Ansible.
  • Podejście "Automation First" – dążenie do eliminacji pracy manualnej poprzez skrypty i narzędzia.
Nice to have:
  • Certyfikaty Azure: DevOps Engineer Expert (AZ-400) lub Azure AI Engineer (AI-102).
  • Doświadczenie we wdrażaniu modeli LLM i architektur RAG.
  • Znajomość narzędzi do monitoringu (Prometheus, Grafana, Azure Monitor).
  • Rozumienie zagadnień sieciowych w chmurze hybrydowej (VPN, VNet, Private Endpoints).
  • Znajomość baz wektorowych (np. w kontekście Azure AI Search).
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