Hiring MLOps Engineer For Global Payment Domain organization - PUNE

MLOps Engineer (Exempt) Enterprise AI/ML Organization OVERVIEW We are looking for an experienced MLOps Engineer to support our AI and ML initiatives, including GenAI platform development, deployment automation, and infrastructure optimization. You will play a critical role in building and maintaining scalable, secure, and observable systems that power scalable RAG solutions, model training platforms, and agentic AI workflows across the enterprise.

RESPONSIBILITIES

• Design and implement CI/CD pipelines for AI and ML model training, evaluation, and RAG system deployment (including LLMs, vectorDB, embedding and reranking models, governance and observability systems, and guardrails).
• Provision and manage AI infrastructure across cloud hyperscalers (AWS/GCP), using infrastructure-as-code tools -strong preference for Terraform-.
• Maintain containerized environments (Docker, Kubernetes) optimized for GPU workloads and distributed compute.
• Support vector database, feature store, and embedding store deployments (e.g., pgVector, Pinecone, Redis, Featureform. MongoDB Atlas, etc).
• Monitor and optimize performance, availability, and cost of AI workloads, using observability tools (e.g., Prometheus, Grafana, Datadog, or managed cloud offerings).
• Collaborate with data scientists, AI/ML engineers, and other members of the platform team to ensure smooth transitions from experimentation to production.
• Implement security best practices including secrets management, model access control, data encryption, and audit logging for AI pipelines.
• Help support the deployment and orchestration of agentic AI systems (LangChain, LangGraph, CrewAI, Copilot Studio, AgentSpace, etc.).

• Must Haves:

  4+ years of DevOps, MLOps, or infrastructure engineering experience. Preferably with 2+ years in AI/ML environments.
• Hands-on experience with cloud-native services (AWS Bedrock/SageMaker, GCP Vertex AI, or Azure ML) and GPU infrastructure management.
• Strong skills in CI/CD tools (GitHub Actions, ArgoCD, Jenkins) and configuration management (Ansible, Helm, etc.).
• Proficient in scripting languages like Python, Bash, -Go or similar is a nice plus-.
• Experience with monitoring, logging, and alerting systems for AI/ML workloads.
• Deep understanding of Kubernetes and container lifecycle management. Bonus Attributes:
• Exposure to MLOps tooling such as MLflow, Kubeflow, SageMaker Pipelines, or Vertex Pipelines.
• Familiarity with prompt engineering, model fine-tuning, and inference serving.
• Experience with secure AI deployment and compliance frameworks

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