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 models
Model 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.