
Hiring MLOps Engineer For Global Payment Domain organization
5 hours ago
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
If anybody interested can share their updated CV on ",com"" or feel free to reach me out at " ".
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