AI/ML Engineer

About the Role

This role is for a hands-on AI/ML Engineer who will design, build, and deploy production-grade Machine Learning and Generative AI solutions. The role focuses heavily on LLM-based applications, including prompt engineering, document processing pipelines, and embedding-based search solutions. The engineer will work with both structured and unstructured data, building pipelines for document extraction, parsing, and chunking, and integrating ML models with Vector Databases and MongoDB.

• Not a pure data analyst; this is an engineering-focused ML/GenAI role.
• Not theoretical AI; requires real-world deployment experience.

Key Responsibilities

• Design and implement AI/ML solutions using Python and modern ML frameworks.
• Develop and optimize Prompt Engineering strategies for LLM-based systems.
• Build and deploy Retrieval-Augmented Generation (RAG) pipelines.
• Integrate LLMs via APIs (Azure OpenAI preferred) into enterprise applications.
• Develop and orchestrate Agentic AI workflows with tool/function calling.
• Implement vector search solutions using Vector Databases.
• Ensure CI/CD integration and cloud deployment (Azure preferred).
• Establish observability, monitoring, and evaluation frameworks for AI systems.
• Collaborate with cross-functional teams to deliver production-ready AI features.

Requirements & Tech Stack

• Python (Expert level)
• Machine Learning & Model Training
• Training, evaluation, fine-tuning
• Tagging and labeling workflows
• Generative AI & LLMs
• Prompt engineering for LLM-based applications
• Document Processing
• Document extraction, parsing, and chunking
• Handling structured & unstructured data
• Embeddings & Vector Search
• Embedding generation
• Vector database integration
• Databases:
• Vector Databases
• MongoDB
• Production-grade ML Engineering
• Scalable, production-ready ML/GenAI solutions

Qualifications

• BACHELOR OF COMPUTER SCIENCE
• Ability to explain complex ML/GenAI concepts to non-technical stakeholders and collaborate effectively with cross-functional teams.
• Strong analytical thinking to break down ambiguous business problems into workable ML or GenAI solutions.
• Takes end-to-end responsibility for solutions—from design to production readiness—without constant supervision.
• Works well with data engineers, product owners, and platform teams to deliver integrated, scalable solutions.
• Actively keeps up with evolving ML, LLM, and GenAI technologies and improves skills proactively.