. AI Model Design & Development

· Build and validate machine learning and generative AI models for a variety of business use cases.

· Experiment, evaluate, and compare algorithms and architectures.

· Work with domain experts to understand expected performance and constraints.

2. Industrialization & MLOps

· Implement production-grade workflows for data preparation, model training, deployment, monitoring, and retraining.

· Apply MLOps best practices for reproducibility, traceability, explainability, and governance.

· Package models for serving in cloud or edge environments.

3. Production Operations & Monitoring

· Monitor model and data drift, model performance, SLA, and cost usage.

· Implement model observability tools and alerts.

· Lead model retraining strategies and continuous improvement cycles.

4. Collaboration & Ecosystem Integration

· Partner with data engineers, software teams, and AI platform teams to integrate models in products and applications.

· Contribute reusable components, libraries, and documentation to accelerate adoption across teams.

· Engage with stakeholders to understand needs, communicate results, and support deployment.

5. Quality, Security & DevSecOps

· Follow secure coding, model governance, and DevSecOps standards.

· Ensure CI/CD pipelines, testing frameworks, and infrastructure as code are applied consistently.

Required Skills & Experience

Technical

· 4–7+ years of experience in AI/ML model development or MLOps.

· Strong software engineering skills (Python; testing; debugging; CI/CD).

· Solid understanding of ML techniques (supervised, unsupervised, NLP, GenAI, time series).

· Experience with MLOps tools such as MLflow, Databricks, model registries, feature stores.

· Experience deploying AI solutions to cloud environments (Azure, Databricks).

· Knowledge of containers, Kubernetes, Terraform, and cloud security best practices.

Data Engineering Basics

· Ability to build or adapt data pipelines and work with structured and unstructured data.