Lead DevOps Engineer

Key Responsibilities:

• Design, build, and maintain CI/CD pipelines for GenAI model development, training, and deployment.
• Automate provisioning and scaling of infrastructure for GenAI workloads using tools like

  Terraform

  ,

  Ansible

  , or

  Pulumi

  .
• Optimize GPU/TPU usage and monitor model performance in production environments.
• Integrate GenAI workflows (e.g., LLM fine-tuning, embedding generation, RAG pipelines) into production-grade infrastructure.
• Collaborate with ML Engineers, Data Scientists, and Platform Engineers to ensure reproducibility, versioning, and traceability of models and datasets.
• Implement observability for GenAI workloads (logging, monitoring, alerting).
• Ensure best practices around security, compliance, and cost optimization in cloud and hybrid environments.

Required Skills & Qualifications:

• 5+ years of DevOps / MLOps experience, with at least 12 years in GenAI-focused environments.
• Strong proficiency in automation tools:

  Docker, Kubernetes, Terraform, Jenkins/GitLab CI, Helm

  .
• Hands-on experience with

  LLMs

  ,

  transformer-based models

  , or GenAI frameworks (e.g., Hugging Face, LangChain, OpenAI, or similar).
• Deep understanding of

  cloud platforms

  (AWS/GCP/Azure), especially AI/ML services like Sagemaker, Vertex AI, or Azure ML.
• Experience in monitoring large-scale systems (Prometheus, Grafana, ELK).
• Familiarity with model/version management tools (MLflow, Weights & Biases, DVC).
• Strong scripting skills – Python, Bash, or Go.

Preferred Qualifications:

• Experience with

  RAG (Retrieval-Augmented Generation)

  pipelines.
• Exposure to

  LLMOps

  , prompt management tools, or GenAI orchestration platforms (e.g., LangServe, BentoML, FastAPI).
• Experience with enterprise security standards for ML environments.

Soft Skills:

• Strong problem-solving mindset and ownership of delivery.
• Ability to lead infrastructure design discussions for GenAI projects.
• Effective communicator, able to work cross-functionally across AI, data, and DevOps teams.

What You’ll Get:

• Opportunity to shape and scale GenAI infrastructure in production.
• Work with state-of-the-art models and tools.
• Competitive compensation, flexible working environment, and growth opportunities.