Job Summary

This role is accountable for building, deploying, and maintaining robust machine learning pipelines and operational frameworks. The individual applies solid expertise in ML Ops and DevOps tools to automate workflows, optimize model lifecycle management, and ensure reliable delivery of ML solutions. They contribute to project success by implementing best practices, supporting process compliance, and providing technical input within the team.

Key Responsibilities

1. Implement and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment processes.
2. Apply DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and monitor pipeline health.
3. Utilize infrastructure-as-code tools such as Terraform and AWS CloudFormation to provision and manage scalable cloud resources for ML workloads.
4. Integrate monitoring solutions like Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production environments.
5. Ensure process compliance by using Git, GitHub, GitLab, and Bitbucket for version control and code management within the team.
6. Participate in technical discussions and feasibility studies to evaluate technical alternatives and support architecture best practices for ML Ops solutions.
7. Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities.

Skill Requirements

Python - strong hands-on Python: FastAPI, Streamlit, pandas, asynchronous programming (asyncio, thread pools), building scalable production-grade applications

o RAG and embeddings - chunking, vector search

o Agent orchestration - LangGraph (or similar) state machines, tool-use loops, verifier patterns

o Classical ML - anomaly detection, time-series baselines, scikit-learn classification, evaluation on imbalanced data

o SQL - Dremio

o Azure

o Testing - pytest, fixtures/record-replay

· Knowledge of Dremio is preferred but not mandatory.

· 8+ years of experience working in Data Management systems with a strong focus on Python-based solutions.

· Proficiency in reference Data Management systems and experience in sql.

ROLES

Role Quantity Location

Python AI/ML Developer Level III 1 India

Other Requirements

1. Optional but valuable:
2. AWS Certified DevOps Engineer
3. - Google Professional Machine Learning Enginee