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