Associate Lead - AI Engineer

The Associate Lead - CAI Delivery Manager leads the Agents pod of Bajaj Finance's Consumer AI programme, which builds an AI-first customer experience on our app and web and inside frontier AI platforms. The pod delivers journeys that AI agents carry out inside the conversation, such as loan applications, EMI card, payments and servicing. The role combines product ownership, delivery management and hands-on functional and technical solutioning: it identifies where AI agents and solutions create value, designs how they work safely within BFL systems and regulation, and leads a team of Product & Delivery Managers to ship and improve them. Duties and Responsibilities

1. Pod Leadership & People Management (Core)

• Lead, coach and develop a team of three Product & Delivery Managers (GB04); allocate work, review output and set quality standards.

• Plan pod capacity, contribute to hiring and run regular performance and development conversations.

2. Product Ownership & Roadmap - Agents Pod

• Own the roadmap and backlog for agent-led journeys, prioritised by customer impact, business value, risk and MVP scope.

• Build business cases for new agentic journeys, with expected benefits, costs (including AI inference) and success metrics, aligned with the shared platform and other pods.

3. AI Use-Case Discovery & Solutioning

• Identify and assess where AI interventions add value across BFL journeys: AI agents, retrieval-augmented answers, document and image understanding, classification, voice and recommendations.

• Define how each agent behaves: goals, the tools and systems it may call, the checkpoints where the customer must confirm, and when it hands over to a human.

• Assess feasibility, ROI and risk for each use case, recommend build, buy or partner options, and keep the team current on AI capabilities (LLMs, agent frameworks, MCP, evaluation).

4. Functional Solution Design

• Map current and future customer journeys and business processes, and write functional specifications (BRD / FRD) for agentic journeys.

• Define business rules, exception handling, customer checkpoints (KYC, consent, KFS, e-sign) and acceptance criteria with business and compliance teams.

5. Technical Solution Design

• Work with architects and engineering to design the technical solution: API integrations with loan origination, loan management, payments and CRM systems; tool and action orchestration; data flows and sequence diagrams.

• Specify non-functional requirements (latency, availability, scale, security, idempotent execution, audit logging) and review technical designs against them.

• Evaluate model, platform and vendor options with engineering, including cost, latency and data-residency trade-offs.

6. Delivery & Program Management

• Run multi-team release plans across internal engineering, platform teams and technology partners; manage cross-pod dependencies.

• Own estimation, scope, timelines, risks and issues (RAID), and agree vendor SOWs and milestones.

• Own release quality gates: evaluation results, UAT, compliance sign-off, go-live and rollback readiness.

7. AI Quality, Risk & Compliance

• Define evaluation frameworks for agents (task success, correct tool use, groundedness, safety) and monitor them in production.

• Make sure agentic journeys have step-up authentication, confirmation before actions, audit trails and locked regulated content (rates, fees, KFS, consent).

• Work with compliance, risk, legal, information security and grievance teams to meet RBI digital lending and responsible-AI expectations and the DPDP Act.|8. Stakeholder Management & Communication

• Be the single point of accountability for the Agents pod, presenting progress, risks and decisions in governance and leadership reviews.

9. Metrics & Continuous Improvement

• Own pod outcomes: journey completion, conversion against the existing app flow, resolution without handoff, cost per resolved request and customer satisfaction.

• Use production data, conversation analytics and incidents to prioritise improvements. Key Decisions / Dimensions

• Agents pod roadmap, prioritisation and sequencing within the agreed MVP scope.

• Solution approach for each use case (build, buy or partner; model and integration pattern), together with architecture.

• Go / No-Go recommendation for releases, based on evaluation, UAT and compliance sign-off.

• Work allocation within the pod and acceptance of partner and vendor deliverables.

• Escalation of risks and cross-pod conflicts to the Delivery Head.Major Challenges

• Making agent-led journeys safe and compliant where the customer must stay in control at regulated steps.

• Turning fast-moving AI capabilities into feasible, measurable use cases that BFL systems can support.

• Coordinating engineering partners, platform teams and other pods with shared dependencies.

• Balancing hands-on solutioning with leading and developing the team. Required Qualifications and Experience

Educational Qualifications

• B.E. / B.Tech in Computer Science, Information Technology, or related discipline.

• MBA from a recognised institution (preferred).

Skills

Core product and delivery skills (as for GB04)

• Hands-on experience of the Product and Software Development Life Cycles (PDLC / SDLC) in Agile / Scrum teams, having run sprints and releases end to end.

• Ability to write and review PRDs, user stories, acceptance criteria and functional specs.

• Delivery management: release planning, estimation, dependency and risk management (RAID), UAT and go-live.

• Data-driven decision-making: funnels, A/B tests, analytics tools or SQL.

• Excellent communication and stakeholder management across business, engineering, design and compliance.

AI depth

• Deep working knowledge of AI: LLMs, prompting, retrieval-augmented generation, AI agents and tool calling, evaluation, guardrails and their limitations.

• Proven ability to identify, size and design AI use cases and interventions (agents, document AI, voice, recommendations) with measurable business outcomes.

• Familiarity with agent frameworks and protocols (for example LangGraph, Amazon Bedrock Agents, Model Context Protocol).

Functional & technical solutioning

• Functional solutioning: process mapping, BRD / FRD, use-case and exception design.

• Technical solutioning: APIs and integration patterns, microservices and event-driven systems, data flows, sequence diagrams and non-functional requirements; able to read API specs and review technical designs.

Leadership

• Team leadership: coaching, work allocation, reviews and hiring.

• Estimation and vendor management: SOWs, milestones and acceptance.

a) Work Experience

• 7-9 years in product, program or delivery management of technology products.

• At least 2 years leading a team or workstream, and at least 2 years hands-on with AI / ML or generative-AI products.