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Tier-1 consulting brand, mid-level (5–7 yrs), metro location, and generalist MLOps skillset increases candidate competition.
MLOps and Python skills transfer well, but Denodo and banking domain bias raise specificity.
Explicit 5–7 year requirement plus mandatory MLOps, Denodo, Airflow, Kubernetes, and CI/CD skills.
Drive industrialization of AI solutions ensuring reliability, scalability, and alignment with best practices.
Act as bridge between Data Scientists and IT Production teams to deliver production-ready ML pipelines and manage Python environment security and obsolescence.
Collaborate with Brussels teams on data sourcing, MLOps excellence, code quality, and cross-team AI service deployment.
5.1-7 years overall experience; minimum 4 years professional Python programming experience (OOP, code quality, security, performance optimization).
Experience with Python environment building tools (pip, mamba, micromamba), MLOps, model versioning, deployment, and containerization (Docker, Kubernetes).
Experience with CI/CD pipelines (Jenkins, GitLab CI/CD), Linux/Cloud infra, Bash scripting, troubleshooting, and database systems (PostgreSQL, query optimization).
Proficiency in Denodo platform (data virtualization, VQL, integration), Apache Airflow DAG development and optimization; Bachelor’s degree or equivalent; Chennai location; strong English communication skills for remote collaboration.
Experienced ML Engineer with strong operational skills in MLOps, AI solution deployment, and Python environment management.
Capable of working cross-functionally in an international, multicultural, and agile setting, engaging frequently with Brussels-based teams.
Technically strong in data virtualization and orchestration tools, with a strategic mindset toward automation and continuous improvement.