Technical PM- AI

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 acrossbanking 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.

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With no forced hierarchy at Capco everyone has the opportunity to grow as we grow taking their career into their own hands.

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We believe that diversity of people and perspective gives us a competitive advantage.

Job Location: Pune

Technical Delivery Lead / Tech PM - AI Solutions POD (ML Gen AI RAG)

Exp: 8 - 10 years in technology delivery including AI/ML initiatives

Role Summary:

We need a hands-on Technical Delivery Lead/Tech PM to drive delivery for an AI solutions Pod building production-grade AI capabilities (ML and Gen AI) including RAG-based solutions. The role requires someone who can plan and deliver based on component interactions (data --> embeddings/vector stores --> retrieval --> LLM orchestration --> evaluation --> deployment) set guardrails and guide decisions on when ML/Gen AI is appropriate vs when it isnt.

This person should be trusted to represent the pod in senior forums and be able to run delivery across multiple pods/squads when needed.

Key responsibilites:

1. Own end-to-end delivery of AI based solutions: roadmap milestones dependency management delivery governance across 1-2 pods
2. solution planning based on architecture: create delivery plans that reflect how components integrate (data ingestion vectorization retrieval model endpoints orchestration UI/APImonitoring)
3. Stakeholder Leadership: represent the team in architecture reviews governance and senior stakeholder updates provide crisp reporting and decision options.
4. Delivery excellence: manage risks NFRs (latency resilience security) release planning production readiness incident learnings
5. Technical oversight of MLGen AI:

- Differentiate and select approaches: classical ML vs Gen AI vs hybrid patterns (eg: RAG ML ranking / classification)

- Define where ML adds value (prediction scoring classification) and where Gen AI adds value (generation summization extraction conversational interfaces)

1. RAG delivery Leadership

- Chunking strategies embedding model selection indexing retrieval patterns reranking citation/attribution freshness updates

- work with teams on relevance evaluation and hallucination reduction patterns

1. Guardrails and controls

- Define Guardrails for data usage sensitive data handling access controls content safety prompt/response filtering and human-in-the-loop where required

- Drive policies/standards for model usage tool access logging monitoring and approval gates

**Must have experience and capabilities:**

1. 8-10 years in technical delivery / engineering-led programme delivery
2. Proven delivery of multiple AI use cases into production (not only POCs)
3. Hands-on technical comfort: able to work with engineers on design decisions challenge approaches and translate requirements into implementable epics/stories
4. strong understanding of

- ML Lifecycle (data prep training/validation bias considerations evaluation deployment monitoring drift)

- GenAI Lifecycle (model selection orchestration prompt strategies at a high level evaluation safety)

- RAG Patterns (vector stores embeddings retrieval reranking grounding citations)

1. Working knowledge of cloud architecture and CI/CD for AI systems (MLOps/ LLMOps concepts)
2. Solid grounding in data engineering concepts: data quality data lineage metadata batch/stream ingestion access controls.

**Preferred skills:**

1. Experience with model risk / governance in regulated environments
2. Familiarity with vector databases and search (eg: Elasticsearch/opensearch vector Pinecone Weaviate pgvector - depending on bank standards)
3. Ability to define and track AI KPIs: accuracy/relevance latency cost per request adoption defect leakage incidents

**What success looks like (outcome-based)**

- AI solutions shipped with measurable business impact not just demos

- clear realistic plans that account for data readiness integration complexity

- Guardrails and controls in place so solutions are safe compliant and supportable

- Stakeholders trust the lead to make decisions and keep delivery moving

If you are keen to join us you will be part of an organization that values your contributions recognizes your potential and provides ample opportunities for growth. For more information visit . Follow us on Twitter Facebook LinkedIn and YouTube.