Associate Consultant / Consultant - AI Engineering & Generative AI Solutions

Location: Mumbai, India

Experience: 1-6 Years

Grade: Associate Consultant / Consultant

About the Role

We are looking for highly skilled AI Engineers and Consultants with strong expertise in Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), Machine Learning, and Python development. The ideal candidate will play a key role in designing, building, and deploying enterprise-scale AI solutions focused on Risk, Treasury, and business transformation initiatives.

This role requires hands-on experience in developing production-ready AI applications, integrating open-source foundation models, optimizing AI workloads for secure on-premises environments, and driving innovation through emerging AI technologies.

Key Responsibilities

AI Solution Development

• Design, develop, and deploy end-to-end AI applications by integrating LLMs, APIs, enterprise data sources, and user interfaces.
• Build scalable and production-ready solutions leveraging Generative AI, Agentic AI, RAG, GraphRAG, and foundation models.
• Develop AI-powered applications for forecasting, information retrieval, document intelligence, and process automation.
• Implement robust evaluation frameworks to assess model performance, response quality, accuracy, and business impact.Generative AI & Agentic Workflows
• Design and implement intelligent agent-based workflows using frameworks such as LangChain and LangGraph.
• Develop Retrieval-Augmented Generation (RAG) and GraphRAG solutions for enterprise knowledge management and decision support.
• Create prompt engineering strategies to improve solution performance, reliability, and user experience.
• Optimize AI agents for complex reasoning, workflow orchestration, and autonomous task execution.Model Engineering & Optimization
• Customize and optimize open-source LLMs, OCR, and document intelligence models for enterprise deployment.
• Adapt GPU-centric AI models to CPU-constrained and secure on-premises environments.
• Implement techniques such as:
• Quantization
• Model compression
• Memory optimization
• Batching
• Caching
• Performance tuning
• Evaluate emerging AI architectures, foundation models, and open-source solutions.Data Engineering & Integration
• Build and maintain scalable data ingestion and ETL pipelines.
• Integrate structured and unstructured data from internal and external sources using APIs, web scraping, and automation frameworks.
• Utilize tools such as BeautifulSoup (BS4), Selenium, and REST APIs for data acquisition and enrichment.
• Ensure data quality, governance, and efficient data processing for AI applications.Research & Innovation
• Analyze research papers, technical publications, and open-source repositories to identify emerging AI capabilities.
• Prototype and evaluate new LLMs, OCR technologies, document intelligence platforms, and foundation models.
• Recommend innovative solutions to address business and technical challenges.Documentation & Governance
• Create and maintain technical documentation, architecture diagrams, deployment guides, and operational runbooks.
• Support solution reviews, code quality assessments, and production readiness activities.
• Ensure compliance with enterprise security, governance, and deployment standards.

Mandatory Requirements

Programming & AI Development

• Strong hands-on programming experience in Python.
• Experience with:
• Pandas
• Polars
• PyTorch
• LangChain
• LangGraph
• FastAPI
• Streamlit
• Ability to build modular, scalable, maintainable, and production-grade AI applications.Generative AI & Foundation Models
• Strong experience with:
• Retrieval-Augmented Generation (RAG)
• GraphRAG
• Agentic AI frameworks
• Vector databases and semantic search
• Experience working with:
• TabPFN or similar tabular foundation models
• TimesFM or similar time-series foundation modelsModel Optimization
• Experience reviewing, modifying, and deploying open-source LLM and OCR codebases.
• Strong understanding of:
• Quantization
• Model compression
• Memory optimization
• Inference acceleration
• Resource-constrained deployments
• Experience deploying models within secure and on-premises enterprise environments.

Preferred Skills

• Prompt engineering and LLM evaluation techniques.
• Experience with OCR and document intelligence solutions.
• Knowledge of AI application monitoring and model observability.
• Understanding of vector databases such as FAISS, ChromaDB, Pinecone, or Milvus.
• Familiarity with Docker, Kubernetes, CI/CD pipelines, and cloud platforms.
• Experience working in Risk, Treasury, Banking, or Financial Services domains.
• Ability to interpret and implement cutting-edge AI research into practical business solutions.