Cloud-based software solutions for infrastructure project management, budgeting, and asset lifecycle.
Enforce requirements for model explainability, interpretability, robustness, and technical design reviews.
Create guidelines for data lineage, feature stores, metadata management, and model repository practices.
Design and maintain scalable MLOps pipelines with CI/CD, automated testing, monitoring workflows, and robust versioning for models, data, and experiments.
Collaborate cross-functionally and provide technical leadership, guidance, and mentorship to engineering, data science, product, and governance teams.
Establish governance checkpoints for model validation, approval, reproducibility, traceability, and
Produce and maintain comprehensive technical documentation (model cards, datasheets, evaluation reports, architecture diagrams, lifecycle logs).
Ensure audit readiness and support internal governance, regulatory, and customer audit requirements while mitigating technical risks and AI vulnerabilities.
Coordinate model retraining, calibration, updates, and sunset workflows based on monitoring and lifecycle needs.
Define and maintain technical standards for the full ML lifecycle, including data sourcing, quality, feature engineering, training, evaluation, deployment, monitoring, and retirement.
Build monitoring frameworks covering model performance, data drift, concept drift, fairness, anomalies, and related alerts.
Experience creating engineering standards, reviewing technical designs, or managing lifecycle governance.
Technical Skills
Bachelor’s or master’s degree in computer science, AI/ML, Data Science, or a related field.
Preferred Certifications
Familiarity with governance frameworks (NIST AI RMF, ISO/IEC 42001) is a plus.
Practical understanding of responsible AI topics (bias, fairness, explainability, robustness).
Governance & Quality Skills
Excellent technical documentation and communication skills.
Google Responsible AI Professional Certificate
Solid grasp of data engineering principles, feature stores, and metadata management.
Microsoft Responsible AI Certification (RAI Engineer)
Soft Skills
Strong software engineering fundamentals (Python, APIs, DevOps, CI/CD).
Hands-on experience with MLOps tools such as MLflow, Kubeflow, Azure ML, Vertex AI, or AWS SageMaker.
Strong analytical and problem-solving mindset.
AWS Machine Learning Specialty
Experience with model monitoring, drift detection, and AI observability tools.
5–10+ years of experience in ML Engineering, MLOps, Data Engineering, or related roles.
Strong understanding of ML/AI concepts, model architectures, training methodologies, and data workflows.
Experience in enterprise SaaS or high-assurance sectors is advantageous.
Ability to lead cross-functional technical discussions and influence engineering decisions.