- Company Name
- Myticas Consulting
- Job Title
- MLOps Consultant, Senior (34474)
- Job Description
-
Job title: Senior MLOps Consultant (Agentic AI Engineering Delivery)
Role Summary: Design, build, and operationalize production‑grade Agentic AI and large‑scale ML workflows within secure enterprise environments. Lead end‑to‑end MLOps pipelines, ensure scalability, reliability, and observability, and integrate solutions on major cloud platforms.
Expactations: • 7+ years of hands‑on engineering in MLOps, DevOps, or data engineering focused on delivery.
• Proven experience shipping Agentic AI/ML systems to production.
• Strong Python programming skill set for production‑grade development.
• Deep understanding of the full ML lifecycle: versioning, monitoring, governance.
• Expertise in containerization (Docker, Kubernetes) and CI/CD automation (GitLab CI, Jenkins, ArgoCD, etc.).
• Experience with GenAI frameworks (LangChain, Hugging Face, OpenAI APIs) for enterprise deployments.
• Familiarity with cloud‑native ML platforms: AWS SageMaker, Google Cloud Vertex AI, or Azure ML.
• Ability to collaborate with data science, engineering, and cloud architecture teams in secure, regulated environments.
Key Responsibilities:
• Architect, implement, and optimize end‑to‑end MLOps pipelines for training, testing, deployment, and monitoring.
• Build, ship, and maintain Agentic AI systems and large‑scale ML workflows emphasizing automation and reliability.
• Develop robust platform engineering solutions to support high‑performance ML/GenAI workloads.
• Manage containerized AI workloads using Docker and Kubernetes for efficient orchestration and scaling.
• Integrate ML pipelines into cloud and enterprise data ecosystems (AWS, Azure, GCP).
• Continuously improve observability, model performance, and deployment automation.
• Document reusable frameworks and engineering best practices for delivery excellence.
Required Skills:
• Python programming – production‑grade systems.
• MLOps lifecycle management: model versioning, monitoring, governance.
• Containerization and orchestration: Docker, Kubernetes.
• CI/CD tooling: GitLab CI, Jenkins, ArgoCD, and related pipelines.
• GenAI framework integration: LangChain, Hugging Face, OpenAI APIs.
• Cloud ML platforms: AWS SageMaker, Google Vertex AI, Azure ML.
• Strong analytical, problem‑solving, and communication abilities.
• Preferred experience in financial services or other secure enterprise settings.
Required Education & Certifications:
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field (or equivalent technical experience).
• Relevant certifications in cloud platforms, Kubernetes, or MLOps are advantageous.