Job Description:
- Collaborate with data scientists and business stakeholders to understand use cases and define ML solution work on Proof of Concepts wherever needed
- Engineer and deploy ML models into production using MLOps best practices (model versioning, monitoring, CI/CD, etc.).
- Build & maintain data pipelines and model performance for scalability and maintainability.
- Ensure all models adhere to organizational AI policies, responsible AI practices, and audit requirements.
- Support data exploration, feature engineering, and occasional model building where needed.
- Automate model retraining, testing, and monitoring to ensure performance over time.
- Document ML workflows, governance checkpoints, and risk assessments.
- Partner with DevOps, IT, and security teams to integrate solutions into enterprise platforms.
- The position requires autonomy and reliability in performing duties while maintaining close communication with rest of stake-holders.
Requirements:
Mandatory:
- Have Master's degree in the field of AI / ML and data science with proven ability to design and develop models
- 6+ years of experience in data science and machine learning, with at least 3+ years in ML engineering roles.
- Proven experience in end-to-end ML lifecycle: data wrangling, model development, deployment, and monitoring.
- Strong programming skills in Python (pandas, scikit-learn, TensorFlow/PyTorch, etc.).
- Strong knowledge in NoSQL databases (any experience in Graph database is desirable)
- Experience with MLOps tools: MLflow, TFX, Airflow, Kubeflow, or similar.
- Familiarity with cloud platforms (GCP, AWS, or Azure) for ML deployment.
- Knowledge of data science techniques including supervised/unsupervised learning, NLP, time series, etc.
- Experience with CI/CD pipelines and containerization (Docker, Kubernetes).
- Strong understanding of AI governance, model risk management, and regulatory requirements in AI.
- Ability to communicate technical concepts to non-technical stakeholders.
Preferred skills:
- Experience with Responsible AI frameworks and bias/fairness testing.
- Exposure to feature stores, model registries, and data versioning.
- Knowledge of data privacy, anonymization, and compliance in regulated industries (e.g., banking, healthcare).
Other Professional Skills and Mind-set:
- Ability and willingness to learn and adopt new technologies.
- Strong organizational and communication skills.
- Strong analytical and problem solving skills.
- Awareness of various software development procedures.
- Ability to follow defined procedures.
- Understanding and respect of cultural diversity.
