MLOps Engineer_5+ years

We are looking for an experienced MLOps Engineer to design, build, deploy, and manage enterprise-scale Machine Learning and Generative AI solutions. The ideal candidate should have strong expertise in Python, CI/CD, Docker, Kubernetes (AKS), Azure Cloud, and MLOps platforms to operationalize AI models in production environments.

Key Responsibilities MLOps & AI Platform Engineering

• Design, develop, and maintain end-to-end MLOps pipelines for model training, validation, deployment, monitoring, retraining, and retirement.
• Build and operate scalable AI/ML platforms across development, testing, and production environments.
• Deploy and manage production-grade Machine Learning and Generative AI applications.
• Support both batch and real-time inference workloads.
• Develop production-ready applications and automation using Python.
• Build and maintain Git-based CI/CD pipelines for automated deployments.
• Implement Infrastructure as Code (IaC) using Terraform, ARM Templates, Bicep, or CloudFormation.

Containerization & Cloud

• Containerize AI applications using Docker.
• Deploy and manage workloads on Kubernetes, preferably Azure Kubernetes Service (AKS).
• Design cloud-native solutions on Azure (preferred), AWS, or GCP.
• Implement deployment strategies such as Blue-Green, Canary, and Rolling Deployments.

Generative AI & LLMs

• Develop and deploy LLM-based applications.
• Implement Retrieval-Augmented Generation (RAG) architectures.
• Work with Prompt Engineering, Embeddings, and Vector Databases.
• Integrate AI solutions with Azure OpenAI, OpenAI, Anthropic Claude, or AWS Bedrock.
• Build Agentic AI solutions using frameworks like LangChain, LangGraph, or similar orchestration tools.

Monitoring & Operations

• Implement monitoring, logging, metrics, tracing, and alerting for AI services.
• Monitor model performance, latency, availability, and model drift.
• Provide production support and resolve deployment or infrastructure issues.

Security & Governance

• Implement IAM, RBAC, secrets management, encryption, and network security.
• Ensure enterprise compliance, governance, and audit readiness for AI platforms.

Required Skills

• 5–13 years of experience in MLOps, AI Engineering, or Platform Engineering.
• Strong programming skills in Python.
• Hands-on experience with Linux.
• Experience deploying Machine Learning models into production.
• Expertise with Docker and Kubernetes (AKS preferred).
• Experience building CI/CD pipelines using Azure DevOps, GitHub Actions, Jenkins, or GitLab CI.
• Experience with Infrastructure as Code (Terraform, ARM, Bicep, or CloudFormation).
• Experience with Azure, AWS, or GCP cloud platforms.
• Strong understanding of MLOps lifecycle and model governance.

Preferred Skills

• Azure Cloud (Preferred)
• Azure Machine Learning
• MLflow, Kubeflow, SageMaker, or Vertex AI
• Azure OpenAI/OpenAI/Anthropic/AWS Bedrock
• LangChain, LangGraph, Semantic Kernel, CrewAI
• RAG, Embeddings, Vector Databases (Pinecone, FAISS, ChromaDB, Weaviate)
• Prometheus, Grafana, Azure Monitor, ELK Stack
• Git, REST APIs, FastAPI, Flask
• High-availability AI platform design
• ITIL or Enterprise Service Management knowledge

Mandatory Skills

• Python
• MLOps
• Model Development & Deployment
• CI/CD
• Docker
• Kubernetes / AKS
• Azure Cloud
• Git
• Infrastructure as Code (Terraform/ARM/Bicep)
• Linux