AI Engineer (MT37ST RM 4129)

Job Description:

• Design, train, and deploy machine learning and deep learning models for forecasting, recommendations, personalization, pricing optimization, fraud detection, and demand planning.
• Develop LLM powered solutions (RAG pipelines, agents, copilots) using internal and external data sources.
• Apply NLP, time series analysis, and predictive modeling to large scale enterprise datasets.
  Platform & Engineering
• Build and maintain end to end ML pipelines (data ingestion, training inference monitoring).
• Implement MLOps practices including CI/CD, model versioning, drift detection, and performance monitoring.
• Deploy AI solutions using cloud native services (GCP (preferrerd), Azure or AWS) with containerization (Docker/Kubernetes).
• Data & Analytics
• Partner with data engineers to curate, clean, and transform structured and unstructured data at scale.
• Optimize feature engineering and model performance using distributed computing frameworks (Spark, Ray, etc.).
• Business & Stakeholder Collaboration
• Work closely with product managers, architects, and business leaders to translate business problems into AI solutions.
• Support AI enablement across vendor and reseller ecosystems through insights and automation embedded in Xvantage.
• Governance & Responsible AI
• Ensure models comply with security, privacy, and responsible AI standards.
• Improve model explainability, fairness, and auditability for enterprise and regulatory needs.

Required Qualifications
Technical Skills

• Strong proficiency in Python (NumPy, Pandas, PyTorch/TensorFlow, scikit learn).
• Experience building and deploying production ML models.
• Hands on experience with cloud AI services (such as GCP Vertex AI).
• Knowledge of LLMs, embeddings, vector databases, Google ADK and prompt engineering.
• Experience with REST APIs, microservices, and event driven architectures.

Data & Engineering

• Solid understanding of SQL and data warehousing concepts.
• Familiarity with MLOps tools (MLflow, Kubeflow, Airflow, GitHub Actions, etc.).
• Experience with distributed data processing.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
• 3 to 7 years of experience in AI/ML engineering or applied data science (level dependent).

Preferred Qualifications

• Experience in digital commerce, supply chain, pricing, or distribution domains.
• Exposure to agentic AI frameworks and autonomous workflows.
• Knowledge of enterprise scale data governance and security practices.
  • Experience supporting AI products used by thousands of users globally