(ML OPNS Lead)Machine Learning Operations Lead Engineer

(ML OPNS Lead)Machine Learning Operations Lead Engineer

Company: Epergne Solutions
Job Location: Indore, Madhya Pradesh, India
Employment Type: Full-time
Experience Required: 7+ years

Role :

ML OPS Lead Engineer Job Mode : Remote

Experience : 7+ Years

Notice Period : Immediate / 10 to 15 Days

Experience Required:

7+ years in platform or infrastructure engineering with significant experience in ML Ops, AI, and Cloud (Azure & AWS).

Key Responsibilities:

Design, deploy, and manage scalable, secure, and high-performing cloud-based infrastructures across Azure and AWS.
Lead end-to-end ML Ops lifecycle, including model deployment, monitoring, retraining, and CI/CD integration.
Collaborate with AI/ML, Data Science, and DevOps teams to automate model lifecycle management and streamline ML workflows.
Architect and implement governance, compliance, observability, and security frameworks for ML and GenAI systems.
Drive innovation in Generative AI and Agentic AI ecosystems, integrating services like Azure OpenAI, Bedrock, Anthropic Claude, and OpenAI API.
Implement infrastructure-as-code (IaC) practices using Terraform, Bicep, ARM, or CloudFormation.
Manage networking, IAM, and security configurations across Azure and AWS environments.
Establish monitoring, alerting, and performance dashboards using Grafana, Prometheus, Azure Monitor, and Log Analytics.

Required Technical Skills:

Cloud Platforms:

Azure: Azure AI Services, Azure Search, Azure ML, Databricks, AKS, Azure AI Foundry, Azure AI Hub.

AWS:

Sage
Maker, Bedrock, Lambda, ECS, CDK, CloudFormation.

AI/ML & Generative AI:

Exposure to Generative and Agentic AI ecosystems (Azure OpenAI, Bedrock, Claude, Llama
Cloud, Lang
Chain).
Understanding of token usage, prompt injection, jailbreak risks, and mitigation methods.
Experience with Azure AI Evaluation SDK and AI Red Teaming Prompt Security Scans.
Hands-on experience with Python ML libraries (TensorFlow, PyTorch, Scikit-learn).

DevOps & Automation:

Strong experience with Azure DevOps / AWS Code
Pipeline for CI/CD setup and management.
Familiarity with Docker, Kubernetes, and container orchestration.