Responsibilities

• Build and maintain platform components for ML model training, deployment, serving and monitoring
• Develop and optimize CI/CD pipelines for machine learning workflows
• Implement and support model lifecycle management, including registries and observability tooling
• Design and manage scalable, secure deployments using containerization and Kubernetes
• Enable secure, reusable and automated workflows to enhance ML developer productivity
• Extend platform capabilities to support LLMOps, RAG and agentic AI workloads
• Collaborate with engineering teams to improve reliability, automation and operational maturity
• Apply governance and compliance standards across AI operations
• Participate in presales and client-facing sessions to translate requirements into scalable solutions
• Advocate cloud best practices for reliability, scalability and cost optimization

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering or related discipline
• Experience in delivering machine learning or MLOps systems into production environments
• Proficiency in Python for building services, APIs, scripts and CI/CD automation
• Working knowledge of modern MLOps stacks including experiment tracking and artifact management
• Hands-on experience with orchestration tools (e.g., Kubeflow, Apache Airflow, Metaflow or Prefect)
• Demonstrated skills with Docker, Kubernetes and distributed deployments
• Practical knowledge of Infrastructure-as-Code (Terraform) and a major cloud provider (AWS, Azure or GCP)
• Familiarity with ML model serving, scaling and monitoring frameworks in production
• Strong communications skills to convey technical decisions and engage with clients effectively

Nice to have

• Background deploying Generative AI solutions, LLM inference pipelines or agentic AI systems
• Experience with feature stores, vector databases and retrieval-augmented generation approaches
• Knowledge of AI governance, security and compliance for regulated sectors
• Familiarity with advanced observability and tracing solutions, such as OpenTelemetry or Langfuse
• Consulting or enterprise architecture experience in large-scale AI programs
• Understanding of FinOps strategies for managing GPU/CPU costs in cloud environments
• Certifications in cloud technologies (AWS, Azure, GCP) or Kubernetes (CKA/CKAD)
• Expertise in securing and operationalizing ML/LLM/agent-based systems for enterprise readiness