MLOps / Cloud Deployment Engineer

# MLOps / Cloud Deployment Engineer

> Xenon7 · Hyderabad, India (Hybrid) · Contract · Posted 2026-08-23

**Workplace:** hybrid

**Department:** Novartis

## Description

Our Client's Digital Finance IT is scaling AI and agentic systems in production. We need an **MLOps / Cloud Deployment Engineer** to own the deployment, reliability, observability, and operational scale of these systems in a regulated enterprise environment.

This is a **cloud and platform engineering role** with deep MLOps/LLMOps focus, not a model-building role. You will operate the runway that ML and GenAI systems run on, not build the models themselves.

**What You'll Do**

• 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

## Requirements
**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** — strong
• **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 We're 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)

## Apply
[Apply at Xenon7](https://apply.workable.com/xenon7/j/697456F4B1/apply)
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