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 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.

Work arrangement
No