- Own CI/CD pipelines for ML models, RAG applications, and agentic AI systems - from experiment to production
- Deploy and operate AI workloads on cloud-native ML/AI platforms - AWS Bedrock/SageMaker, Azure AI Foundry / Azure Machine Learning, or equivalent
- Build and maintain observability, tracing, and monitoring for LLM and agentic systems - latency, cost, hallucination rates, tool-call success, drift detection
- Implement model governance and guardrails - approval gates, kill-switches, escalation paths, audit trails
- Manage infrastructure-as-code (Terraform, Bicep, or equivalent) for reproducible AI/ML environments
- Design cost and performance optimization strategies - token usage tracking, caching, model routing, autoscaling, warehouse/cluster right-sizing
- Own security posture - RBAC, secret management (Key Vault / Secrets Manager), prompt-injection risk mitigation, auditability for regulated pharma
- Partner with data engineers, AI engineers, and Finance business stakeholders to move systems from prototype to reliable production
- Implement evaluation frameworks for AI systems in production - regression testing, adversarial testing, accuracy tracking, hallucination monitoring

Must-Have Experience

- 5+ years in cloud/DevOps/MLOps engineering on AWS, Azure, or GCP
- Production deployment of ML or GenAI systems - CI/CD, containerization (Docker/Kubernetes), infrastructure-as-code (Terraform)
- MLOps tooling - MLflow, SageMaker Pipelines, Azure ML Pipelines, or equivalent
- LLM/GenAI operational experience - observability tools (LangSmith,



Weights & Biases, or equivalent), cost monitoring, latency optimization, prompt/model versioning
- Cloud-native AI platforms - hands-on with at least one of: AWS Bedrock, SageMaker, Azure AI Foundry, Azure OpenAI, Vertex AI
- Python, Bash, and infrastructure scripting - robust
- Security and governance in regulated environments - RBAC, secrets, audit, compliance

Nice to Have

- Pharma, life sciences, or regulated financial services domain
- Experience operating agentic AI systems in production - multi-agent orchestration, tool-calling, human-in-the-loop workflows
- LangChain, LangGraph, CrewAI, AutoGen, or Semantic Kernel operational experience
- Kubernetes-native ML platforms (Kubeflow, Ray)
- Snowflake or Databricks operational experience (compute governance, cost management)
- Certifications: AWS/Azure ML Engineer, Kubernetes CKA/CKAD, Terraform Associate

What Were NOT Looking For

- Data Scientists or research engineers - this is a production platform role
- Application developers with light DevOps exposure - need real MLOps/cloud engineering depth
- Pure infra engineers with no AI/ML operational experience - need to understand what makes LLM systems different (evals, hallucinations, prompt versioning, RAG grounding)

Disclaimer: This job description has been sourced from a public domain and may have been modified by Naukri.com to improve clarity for our users. We encourage job seekers to verify all details directly with the employer via their official channels before applying.

📌 MLOps / Cloud Deployment Engineer (Hyderabad)
🏢 Xenon7
📍 Hyderabad