Role & responsibilities

1. AI Project and Program Delivery

Own delivery of multiple AI initiatives across discovery, feasibility, POC, business case, approvals, development, UAT, production release, stabilization and closure.

Create integrated project plans, milestones, work breakdowns, RAID logs, action trackers, status reports and governance updates; manage scope, timeline, cost, quality and dependencies.

Coordinate with business SPOCs, product owners, Data and Analytics, Cloud, DevOps, Infrastructure, Enterprise Architecture, AppSec, InfoSec, Risk, Procurement, Legal and Operations.

Drive BRD and solution requirements, success criteria, acceptance criteria, UAT planning, operational readiness, change requests, release approvals, handover and benefits tracking.

Prepare explicit executive updates covering project status, business outcomes, cost-benefit, decisions required, key risks and recovery actions.

2. AI Solution Development and Technical Ownership

Translate business problems into practical AI solution concepts, user journeys, data flows, APIs, integration requirements and deployment designs.

Build or contribute to prototypes and production components using Python and relevant AI/ML frameworks, APIs and cloud services.

Work on generative AI, LLM, agentic workflows, RAG, document intelligence, conversational AI, prompt workflows and machine learning use cases, as applicable.

Define and execute solution evaluation for accuracy, relevance, latency, reliability, safety, scalability and cost; coordinate defect resolution and iterative improvement.

Review code, technical documents, architecture, logging, monitoring and support models to ensure production readiness and maintainability.

3. AI Governance, Security and Lifecycle Controls

Maintain governance artefacts including AI Bill of Materials,



model and component details, data and infrastructure records, evaluation evidence, SOPs, decision logs and audit trails.

Ensure alignment with enterprise architecture, information security, application security, data privacy, responsible AI, vulnerability management, backup, disaster recovery and change-management requirements.

Build controls for access, secrets, encryption, PII handling, prompt and response logging, guardrails, human-in-the-loop review, model monitoring, drifting, incidents and decommissioning.

Coordinate required forums and approvals, track observations to closure and ensure evidence is complete, current and audit ready.

4. Vendor Evaluation and Stakeholder Management

Assess AI vendors, platforms and models for functional fit, architecture, integration readiness, security, compliance, scalability, implementation effort, support and total cost of ownership.

Plan and run demos and POCs, define objective scorecards and success metrics, document recommendations, and support commercial and contractual evaluation.

Manage vendor deliverables, dependencies, issue resolution, knowledge transfer, support transition and adherence to agreed milestones and controls.

Communicate complex technical topics, options, trade-offs and risks clearly to business and senior stakeholders.

Preferred candidate profile

- Education: Bachelors or Masters degree in Engineering, Computer Science,



Information Technology, Data Science, AI/ML, or a related discipline from a recognized institution.
- Experience: 4-7 years of relevant experience, including at least 2 years in AI/ML, GenAI, data science, intelligent automation, or AI solution delivery. BFSI domain experience preferred.
- Project Delivery: Proven experience managing end-to-end technology and AI projects using Agile, Scrum, hybrid, or structured delivery methodologies, with strong planning, RAID, governance, vendor, and stakeholder management capabilities.
- AI Engineering: Hands-on expertise in Python, API integrations, GenAI/LLMs, RAG, agentic AI, document processing, conversational AI, and machine learning solutions, supported by practical project delivery experience.
- Cloud & Operations: Working knowledge of AWS services, compute, storage, databases, networking, IAM, security, monitoring, CI/CD pipelines, containers, and serverless deployments.
- Governance & Risk: Sound understanding of AI lifecycle governance, responsible AI, data privacy, security testing, auditability, model evaluation, monitoring, and production support.
- Communication: Strong written and verbal communication skills with the ability to engage effectively with technical, business, risk, vendor, and senior leadership stakeholders.
- Preferred Certifications: PMP, PRINCE2, Agile/Scrum, cloud, or AI certifications are desirable but not mandatory.
- Demonstrated Capability: Ability to showcase delivery ownership and practical AI expertise through real-world implementations, prototypes, repositories, case studies, or equivalent evidence of successful project execution.

Note: Mumbai-based candidates only. This is a full-time Work-from-Office role.

📌 Manager, AI (Mumbai)
🏢 SMFG INDIA CREDIT
📍 Mumbai