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Lead AI Engineer, Pune – helm Document AI, Agentic workflows & RAG on GCP.
Drive transformative AI for banks & energy sectors in India's leading consulting firm.
About Us
“Capco, a Wipro company, is a global technology and management consulting firm. Awarded with Consultancy of the year in the British Bank Award and has been ranked Top 100 Best Companies for Women in India 2022 by Avtar & Seramount. With our presence across 32 cities across globe, we support 100+ clients across banking, financial and Energy sectors. We are recognized for our deep transformation execution and delivery.
WHY JOIN CAPCO?
You will work on engaging projects with the largest international and local banks, insurance companies, payment service providers and other key players in the industry. The projects that will transform the financial services industry.
MAKE AN IMPACT
Innovative thinking, delivery excellence and thought leadership to help our clients transform their business. Together with our clients and industry partners, we deliver disruptive work that is changing energy and financial services.
#BEYOURSELFATWORK
Capco has a tolerant, open culture that values diversity, inclusivity, and creativity.
CAREER ADVANCEMENT
With no forced hierarchy at Capco, everyone has the opportunity to grow as we grow, taking their career into their own hands.
DIVERSITY & INCLUSION
We believe that diversity of people and perspective gives us a competitive advantage.
Job Details
Lead AI Engineer
Location: Pune/Mumbai/Hyderabad/Chennai/Bengaluru/Gurugram
AI Engineer responsible for designing, building, and deploying production-grade AI solutions on Multi Cloud especially Google Cloud Platform (GCP), with a focus on Document AI (DocAI), Agentic AI workflows, and Retrieval-Augmented Generation (RAG). The role will develop scalable pipelines and AI services using multiple AI offerings to enable semantic search, document understanding, and intelligent automation, partnering closely with product, engineering, data, and risk stakeholders to deliver secure, reliable outcomes.
Key responsibilities
- Design and implement end-to-end DocAI solutions (document ingestion, classification, extraction, validation, and human-in-the-loop review) for structured and unstructured documents.
- Build and orchestrate Agentic AI systems (tool use, planning, memory, guardrails) to automate multi-step business processes and integrate with enterprise systems/APIs.
- Develop RAG architectures for enterprise knowledge retrieval, including chunking strategies, embedding generation, indexing, reranking, and response grounding/citation.
- Implement and operate vector database solutions including schema design, indexing, and performance tuning.
- Develop scalable AI services based on business requirements from the Internal Audit.
- Establish evaluation frameworks for LLM/RAG (quality, hallucination/grounding, latency, cost), and implement monitoring/observability in production.
- Apply security, privacy, and responsible AI practices (data handling, access controls, prompt safety, model governance) aligned to organisational standards.
- Collaborate with architects and platform teams to define reference architectures, reusable components, and CI/CD pipelines for AI delivery.
- Produce clear technical documentation, runbooks, and knowledge transfer; support incident triage and continuous improvement.
- Contribute to engineering best practices: code reviews, testing, performance optimisation, and reliability engineering.
Skills & Experience
- Proven experience building and deploying AI solutions in production, including LLM-based applications.
- Strong hands-on experience with DocAI/document understanding (OCR, extraction, layout parsing) and building document processing pipelines.
- Solid experience implementing RAG systems end-to-end (retrieval, embeddings, vector indexing, reranking, grounding, citations).
- Experience building Agentic AI workflows (function/tool calling, orchestration frameworks, state/memory management, guardrails).
- Strong knowledge of vector databases and semantic search concepts; ability to tune for relevance, latency, and scale.
- Proficiency in Python (must have); experience with common AI frameworks (e.g., LangChain/LlamaIndex or equivalent), and API/service development (FastAPI/ReactJS).
- Strong GCP experience (Good to have) incl. GenAI capabilities, Cloud Run/GKE, BigQuery, Pub/Sub, Cloud Storage, IAM, VPC/networking basics, logging/monitoring.
- MLOps/LLMOps experience: CI/CD, model/version management, automated testing, evaluation pipelines, and production monitoring.
- Software engineering fundamentals: data structures, system design, secure coding, unit/integration testing, and performance optimisation.
- Experience working in regulated environments and applying security/privacy controls (PII handling, encryption, access management) is preferred.
- Bachelor’s/Master’s in Computer Science, Engineering, Data Science, or equivalent practical experience.