MLOps / Cloud Deployment Engineer
Company: Xenon7
Role summary
MLOps / Cloud Deployment Engineer is a contract role at Xenon7 in Hyderabad, Telangana, India (remote). Posted 2026-08-23. Experience level: 5-10.
- Job title
- MLOps / Cloud Deployment Engineer
- Company
- Xenon7
- Location
- Hyderabad, Telangana, India
- Work arrangement
- remote
- Employment type
- contract
- Category
- Software Engineering
- Experience level
- 5-10
- Date posted
- 2026-08-23
You’ll have a great deal of independence in this senior-level remote role, where you can focus on building robust infrastructure and streamlining deployment processes for a growing company. It’s an ideal spot for an experienced engineer looking to make a high-level impact on systems architecture without being tied to a specific office location.
Job 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
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)