AI/Machine Learning Engineer

RESPONSIBILITIES & TASKS

AI Solution Development

• Design, develop, test, and deploy AI/ML models supporting business and operational outcomes.
• Build and maintain AI services, APIs, and microservices for enterprise consumption.
• Develop Generative AI and Agentic AI solutions using approved enterprise platforms and frameworks.
• Implement Retrieval Augmented Generation (RAG) architectures and knowledge retrieval solutions.
• Develop prompt engineering, evaluation, and optimization approaches for AI systems.

AI Platform Engineering and MLOps

• Develop and maintain AI model deployment pipelines and model lifecycle processes.
• Build automated testing, deployment, monitoring, model versioning, and release management capabilities.
• Support production deployment of models and AI services aligned to enterprise architecture standards.

Data Engineering and Integration

• Design and develop data pipelines required for AI model training and inference.
• Integrate AI solutions with enterprise applications, cloud platforms, APIs, databases, and data warehouses.
• Support data preparation, feature engineering, and operationalization of AI models.

AI Operations, Monitoring, and Continuous Improvement

• Monitor model performance, accuracy, reliability, cost, and operational stability.
• Implement observability and monitoring frameworks for AI workloads.
• Investigate production issues, perform root-cause analysis, and implement corrective actions.
• Tune and optimize models and AI services for performance, quality, and usability.

Security, Governance, and Responsible AI

• Ensure AI solutions align with enterprise AI governance, information security, privacy, and data protection requirements.
• Apply responsible AI principles including fairness, transparency, explain ability, reliability, and human oversight.
• Support AI risk assessments, security reviews, and compliance documentation.

Collaboration and Enablement

• Work closely with business stakeholders, Data Scientists, solution architects, application teams, and platform engineers.
• Participate in AI use case discovery, design workshops, technical reviews, and implementation planning.
• Support knowledge sharing and AI capability development across regional subsidaries.

SKILLS & QUALIFICATIONS

• Minimum 8 years of experience in software engineering, AI engineering, machine learning engineering, or related disciplines.
• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Technology, or a related discipline.
• Experience developing production-grade AI/ML solutions, working with cloud-native platforms, and implementing machine learning models in enterprise environments.

Technical Skills

• Python
• Machine learning frameworks such as PyTorch, TensorFlow, and Scikit-Learn
• LLM and agent frameworks such as LangGraph, LangChain, Semantic Kernel, or AutoGen
• API development and microservices
• Vector databases and RAG architectures
• MLOps platforms, CI/CD, and model lifecycle management
• SQL and data engineering
• Cloud services across AWS, Azure, or GCP
• Container technologies such as Docker and Kubernetes
• Git and DevOps practices