AI ML Engineer

Role overview

Design, develop, deploy, and optimize machine learning and generative AI solutions that solve business problems and create measurable impact.

Key responsibilities

• Build, train, evaluate, and deploy ML models.
• Develop data pipelines and feature engineering workflows.
• Work with NLP, LLMs, computer vision, or predictive analytics solutions.
• Fine-tune foundation models and implement RAG architectures.
• Collaborate with product, engineering, and business teams.
• Monitor model performance and implement MLOps best practices.
• Conduct experimentation and model benchmarking.
  
  

Required Skills & Qualifications

• Python (NumPy, Pandas, Scikit-learn).
• Machine learning and deep learning fundamentals.
• PyTorch and/or TensorFlow.
• SQL and data processing.
• Cloud platforms (Azure, AWS, or GCP).
• Git, CI/CD, and MLOps concepts.
• LLMs, prompt engineering, vector databases, and RAG.
  
  

Education

Bachelor's or master's degree in Computer Science, AI, Data Science, Statistics, or related field.

Preferred Skills

• Experience with Azure AI, OpenAI, LangChain, Semantic Kernel, or similar frameworks.
• Exposure to Docker and Kubernetes.
• Knowledge of model deployment and monitoring.
  
  

Success metrics

High-quality model performance, scalable deployments, strong stakeholder collaboration, and continuous improvement of AI solutions.

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