AI Engineer - Product development (Mumbai)

AI Engineer - Product development (Mumbai)

Role Overview
We are looking for an AI Engineer in Mumbai with 3-5 years of relevant experience who can design, build, and deploy a secure, offline, LLM-powered platforms . You will work on everything from model integration and document intelligence pipelines to backend services and system architecture, in close collaboration with product, UI, and client stakeholders.
This role requires robust hands-on engineering skills, deep understanding of NLP/LLMs, and experience working in security-constrained enterprise environments in the BFSI sector.
The ideal candidate has experience taking AI solutions from idea -> implementation -> deployment -> iteration and is comfortable treating AI capabilities as real world product features (not standalone experiments).

Responsibilities
AI & LLM Engineering
Deploy and fine-tune LLMs on on-premise / private infrastructure (no external API dependency)
Build backend architecture by developing NLP pipelines and implement RAG (Retrieval Augmented Generation) pipelines using local vector databases
Optimize models for performance, memory usage, and inference latency on internal hardware
Design AI components with clear product use cases and user workflows in mind
Translate functional requirements into AI capabilities that can be embedded into applications

Document Intelligence
Design and develop secure backend services (Python-based preferred) to orchestrate: Document ingestion, LLM inference, Scoring and comparison logic
Build robust pipelines to process multi-format documents (DOCX, PDF, scanned documents, etc.)
Handle document chunking, embeddings, metadata tagging, and version comparison
Design explainable outputs for document reviewers (traceability to source clauses)
Integrate with Microsoft ecosystem : SharePoint document repositories and MS Office file formats




Ensure the entire system functions fully offline within a restricted network
· Apply cloud computing fundamentals (compute, storage, networking) effectively

Security & Compliance
Follow enterprise-grade security practices: No external data transfer, secure credential and access handling, Role-based access control (RBAC) support
Align implementation with client IT/security requirements
Design systems with logging, monitoring, audits and traceability in mind
Familiarity with confidential computing and private networking patterns is a plus (e.g., Bastion access, Private Link/Private Endpoints, private DNS, Key Vault/secret management).
Ensure compliance with ethical AI practices and regulatory frameworks.

Collaboration & Ownership
Collaborate with frontend engineers to support UI requirements
Participate in solution design discussions with client stakeholders
Own components end-to-end—from POC to production deployment
Contribute to technical documentation of assumptions, risks, decisions and deployment runbooks
Act as a technical interface with client IT and legal stakeholders to clarify requirements and acceptance criteria.
Own end-to-end delivery of assigned AI features/components (scope, milestones, acceptance criteria).
Support demos, walkthroughs, UAT readiness, and handover with documentation/runbooks.

Qualifications
Core Technical Skills




3–5 years of hands-on experience in AI / ML engineering and Strong proficiency in Python
Strong grasp of computer architecture, data structures, system software, and machine learning fundamentals.
Solid understanding of NLP fundamentals, transformer architectures, embeddings and semantic search
Strong experience working with structured and unstructured data, including preprocessing, validation, and transformation for AI pipelines
Hands-on experience with open-source LLMs (e.g., LLaMA, Mistral, Falcon, etc.)
Experience deploying models locally or on private servers (not just cloud APIs)
Experience with frameworks such as Hugging Face, LangChain / LlamaIndex (or similar orchestration frameworks)
Vector databases (FAISS, Chroma, Milvus, etc.)
Experience with prompt engineering and structured outputs
Ability to plan work, estimate, and deliver independently in a client-facing or delivery driven environment.

Systems & Infrastructure
Experience building and deploying backend services (FastAPI, Flask, or similar) in banking, finance or other regulated
Familiarity with Linux environments and GPU-based inference
Experience with working on containerization and clustering (Docker preferred)
Experience working in restricted / air-gapped environments is a big plus
Familiarity with logging, monitoring, and troubleshooting of deployed services
· Experience with information security and secure development best practices.

Education
Bachelor’s or Master’s degree in: Computer Science, Data Science or Artificial Intelligence
Exposure to banking, finance, or regulated enterprise environments
Experience optimizing models for low-latency inference
Familiarity with UI-driven AI workflows (human-in-the-loop systems)

📌 AI Engineer - Product development (Mumbai)
🏢 Quilytics
📍 Mumbai