Job Description

• 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

• 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 candidate must have strong Python expertise and practical experience taking ML and GenAI use cases from development to deployment.
• 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.

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 Agent ic 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

Generic Managerial Skills, If any:

• 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.

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