Generative AI ML Engineer

What you'll work on

• Design, develop, and deploy Generative AI and Machine Learning models for enterprise use cases.
• Build and fine-tune Large Language Models (LLMs) using frameworks such as OpenAI, Azure OpenAI, Hugging Face, or Anthropic.
• Develop AI-powered applications using techniques like prompt engineering, RAG (Retrieval-Augmented Generation), and agent-based workflows.
• Implement end-to-end ML pipelines including data ingestion, model training, evaluation, deployment, and monitoring.
• Develop and optimize ML workflows using Python and ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Build scalable AI services and APIs integrated with backend systems and cloud platforms.
• Deploy and manage models using cloud-native services (Azure ML preferred).
• Implement vector databases and embeddings using tools like FAISS, Pinecone, Chroma, or Azure AI Search.
• Ensure model performance, accuracy, scalability, and cost optimization.
• Apply Responsible AI, data privacy, and security best practices.
• Monitor model drift, bias, and performance in production environments.
• Automate ML workflows using CI/CD and MLOps practices.
• Collaborate with data engineers, software developers, product managers, and business stakeholders.
• Translate business requirements into AI/ML solutions.
• Participate in Agile/Scrum ceremonies including sprint planning, daily stand-ups, reviews, and retrospectives.
• Maintain technical documentation including model architecture, data flows, and deployment strategies.