AI ML Ops Engineer

MLOps / LLMOps Engineering

• Design, build, and maintain end‑to‑end MLOps and LLMOps pipelines covering experimentation, training, deployment, monitoring, and retraining.
• Operationalize ML and LLM models for production environments with high availability and scalability.
• Implement best practices for model versioning, artifact management, and reproducibility.

CI/CD & Automation

• Develop and maintain CI/CD/CT pipelines for ML and GenAI workloads.
• Automate model packaging, deployment, validation, and rollback processes.
• Integrate AI pipelines with enterprise DevOps toolchains.

API & Model Serving

• Design, develop, and deploy REST‑based inference services using Python frameworks such as Flask or FastAPI.
• Enable scalable and secure model serving for batch and real‑time use cases.

Monitoring, Governance & Responsible AI

• Monitor model performance, data drift, and model drift in production environments.
• Support Responsible AI practices including robustness, explainability, and governance controls.
• Enable continuous feedback loops and retraining strategies to maintain model quality.

Collaboration & Delivery

• Collaborate with cross‑functional teams including Data Engineering, Data Science, Platform Engineering, and Cyber/Compliance teams.
• Support enterprise AI platforms and GenAI solutions across multiple client engagement

Skills and Attributes:

Technical Skills

• Strong programming experience in Python.
• Experience building REST APIs for ML/AI services using Flask, FastAPI, or similar frameworks.
• Strong hands‑on experience with CI/CD / CT pipelines for ML workloads.
• Solid understanding of MLOps principles, including model lifecycle management and automation.

Educational Background

• Bachelors / master’s degree in data science, AI, Gen AI, or AI/ML. Engineering or quantitative background preferred.

Professional Experience

• 6-12 years of hands‑on experience in Machine Learning with strong operationalization and production delivery exposure.
• Proven track record in AI-driven transformation programs, including Agentic AI, predictive analytics, and process mining.
• Experience in building and deploying accelerators, playbooks, or proprietary platforms for data-driven consulting.

Soft Skills

• Exceptional client engagement and stakeholder management skills
• Strong problem‑solving skills with the ability to translate complex business requirements into practical, high‑quality technical solutions.
• Excellent communication, presentation, and leadership abilities.

Preferred / Good‑to‑Have Skills

• Experience with LLMOps for Large Language Models and Generative AI workloads.
• Familiarity with ML lifecycle tools (e.g., experiment tracking, model registries, feature stores).
• Proven experience in Azure Ecosystem
• Experience with cloud‑based ML platforms (Azure ML, AWS SageMaker, or equivalent).
• Exposure to monitoring and observability tools for AI systems.
• Understanding of Responsible AI concepts and AI governance frameworks.

Preferred Certifications

• GenAI and LLM focussed, AI/ML, or data analytics certifications (Google, Microsoft, AWS, Coursera, etc.)

Why Join Us

• Be at the forefront of AI-driven innovation across multiple client sectors.
• Work with global clients to drive real business impact.
  • Collaborate with a team of AI experts, analytics leaders, and industry specialists in a highly entrepreneurial environment.