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We are seeking an AI / Machine Learning Engineer to design, build, deploy, and govern scalable ML and AI solutions across the enterprise. This role combines hands‑on model development with strong ownership of AI governance, risk management, and responsible AI practices to ensure models are explainable, secure, compliant, and production‑ready.
Job Responsibility:
Design, develop, train, and deploy machine learning and AI models for structured and unstructured data use cases
Build end‑to‑end ML pipelines including data ingestion, feature engineering, training, evaluation, deployment, and monitoring
Implement MLOps practices for versioning, CI/CD, model lifecycle management, and automated retraining
Collaborate with data engineers, product managers, and business stakeholders to translate requirements into AI solutions
Monitor model performance, drift, bias, and data quality in production environments
Optimize model accuracy, scalability, latency, and cost efficiency
Develop reusable ML components, libraries, and frameworks to accelerate delivery
Embed AI governance controls across the model lifecycle (design, development, testing, deployment, decommissioning)
Ensure models meet enterprise standards for explainability, transparency, fairness, and auditability
Implement model documentation, lineage, and traceability (data sources, features, assumptions, limitations)
Perform model validation activities including bias testing, robustness testing, and performance benchmarking
Support regulatory, compliance, and legal requirements (e.g., model risk management, data privacy, internal audits)
Partner with security teams to ensure secure model development and protection of sensitive data
Contribute to responsible AI policies, standards, and best practices across the organization
Requirements:
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field
4+ years of hands‑on experience building and deploying machine learning models in production environments
Strong programming experience in Python (experience with Java or Scala is a plus)
Solid understanding of machine learning algorithms (supervised, unsupervised, deep learning, NLP, or time‑series)
Experience with ML frameworks and libraries such as TensorFlow, PyTorch, scikit‑learn, or similar
Practical experience with data pipelines, model deployment, and monitoring
Understanding of AI governance, model risk management, or responsible AI concepts
Nice to have:
Experience implementing explainability tools (e.g., SHAP, LIME, model interpretability techniques)
Familiarity with AI governance frameworks or regulatory guidance (e.g., internal model risk policies, industry standards)
Experience with cloud platforms (AWS, Azure, or GCP) and ML services
Exposure to MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, or similar)
Knowledge of data privacy, security, and compliance concepts (PII, GDPR‑like controls, access controls)
Experience working in highly regulated or large‑scale enterprise environments