AI Engineer : Mumbai
Build and deploy enterprise-grade AI systems for a B2B payments platform, focusing on LLMs, RAG, OCR, and Agentic AI using Python, cloud platforms, and MLOps tools.
- Compensation
- Not disclosed
- Location
- Maharashtra, Mumbai, India
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 2+ years of professional software engineering experience focused on Generative AI / LLMbased applications.
- Proven experience in developing and deploying enterprise-grade AI systems.
- Degree in Computer Science, Computer Engineering, or a related field. Demonstrated expertise in LLMs, LangChain, LangGraph, RAG architectures,
- OCR, and Agentic AI frameworks.
- Advanced proficiency in Python and modern AI/ML development practices. Strong experience with cloud platforms (GCP, AWS, Azure).
- Hands-on expertise in MLOps & DevOps (Kubernetes, Docker, Jenkins, Ansible, Infrastructure-as-Code).
- Proficiency in databases and data engineering (PostgreSQL, SQL, Vector Databases).
- Experience with API frameworks such as FastAPI or Spring Boot.
- Excellent analytical, problem-solving, and communication skills, with a proven ability to lead complex AI initiatives.
- Comfortable working across cross-functional and distributed teams. Preferred / General Requirements
- Experience with enterprise AI security, governance, and compliance frameworks.
- Familiarity with frontend integration frameworks (e.g., ReactJS) for AI-enabled applications.
- A passion for innovation and staying current with emerging trends in Generative AI and Agentic systems.
Full description
The Basics:
Team: Engineering
Experience: 2 to 4 years
Location: Mumbai- Andheri East Introduction
$11 trillion of money flows every year between companies in India. It typically takes avg. 70 days for a business to get paid, and it’s increasing 5% every year. Formal funding options are limited, and cover < 5% of the addressable market.
We are building India’s largest B2B Payments Platform that transforms how businesses pay and get paid. Our award-winning platform already processes INR 20,000+ Crores of invoices every month, across 300,000 MSMEs and 1200+ corporates; with 30+ lenders plugged in for credit.
Globally, companies in this space, like Coupa, Bill.com, Melio, C2FO, Tipalti, have witnessed tremendous success over the past decade doing $ Billion+ disbursements weekly and cumulatively valued over $50 Billion.
You will join our core team that currently consists of ex-BCG and ISB / IIM alumni with a team of industry veterans serving on the advisory board. We are backed by Elevation Capital (one of the most successful VCs in India) and General Catalyst ($15 Billion+ global fund, and early investors in Stripe, Airbnb etc.). We share our lineage with HCS, a 25 year old investment bank and a registered NBFC. We are a team of passionate problem solvers and we’re building a technology company with a strong product innovation mindset.
We are looking for someone who loves a challenge, is ambitious, super tenacious and persistent. S/he is a self-starter, thrives in a dynamic, small start-up environment, has a knack for understanding customer needs, and is result-oriented. If you check these boxes - we want to talk to you!
What will a week look like in this role:
We are seeking an experienced Senior AI Engineer to join our Engineering Team.
The ideal candidate will have deep expertise in Large Language Models (LLMs), OCR, RetrievalAugmented Generation (RAG), and Agentic AI systems, coupled with strong experience in cloud-native, enterprise-scale environments. This role is pivotal in driving our AI innovation strategy, leading end-to-end solution development, and delivering production-grade AI systems.
Key Responsibilities
Lead the full lifecycle of Generative AI solutions — from design and development to deployment — encompassing LLM-powered workflows, RAG pipelines, OCR, and Agentic AI systems.
Architect and implement secure, scalable AI infrastructures using GCP, AWS, or Azure.
Apply LLMOps best practices, including fine-tuning, advanced prompt engineering, and model optimization, to maximize performance and contextual accuracy.