ML Ops Technical Specialist - DevOps, Python

Seeking a highly skilled Senior AI Automation Engineer / Technical Lead to design, develop, and deploy intelligent automation solutions for release gating, production issue prevention, ticket auto-resolution, SRE analytics, and Agentic AI-based self-healing systems. The candidate will drive innovation initiatives that improve production quality, reduce defect leakage, automate operational activities, and enhance customer experience through AI-powered decision-making and workflow automation.

Key Responsibilities

1. Architect and implement ML Ops solutions using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, deployment, and monitoring processes.
2. Design and manage CI/CD pipelines for ML projects utilizing Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to ensure seamless integration and delivery of machine learning models.
3. Develop infrastructure-as-code templates with Terraform and AWS CloudFormation to provision and manage scalable cloud environments for ML workloads.
4. Integrate monitoring and logging solutions using Prometheus, Grafana, ELK Stack, and Fluentd to enable real-time performance tracking and issue resolution for ML systems.
5. Lead the adoption of DevOps practices by configuring version control systems such as Git, GitHub, GitLab, and Bitbucket for collaborative development and reproducibility.
6. Serve as a technical SME for ML Ops, providing guidance on best practices, tool selection, and workflow optimization within the team.
7. Mentor and train team members on ML Ops tools, automation strategies, and cloud-native ML pipeline development to build technical capability and mitigate delivery risks.
8. Review and validate project deliverables to ensure alignment with client specifications, quality standards, and industry benchmarks.
9. Recommend and implement client-focused value creation initiatives by leveraging advanced ML Ops frameworks and industry best practices.

Skill Requirements Mandatory Technical Skills AI / GenAI / Agentic AI Hands-on Agentic AI implementation experience Agentic AI architecture and orchestration Multi-Agent Systems LLM integration (Azure OpenAI, OpenAI, Anthropic, etc.) Prompt Engineering Retrieval Augmented Generation (RAG) Knowledge Graphs Vector Databases AI Feedback Loop implementation Self-learning AI systems Autonomous ticket triaging and resolution agents Experience: 3+ years in AI/ML and 1+ year in Agentic AI solutions Python, ML Pipelines, DevOps, Cloud

Other Requirements