Senior Process Manager

Job Description

Senior Process Manager

Chennai, Tamil Nadu

Job Summary

We are seeking an astute, forward-thinking Senior Business Analyst specializing in Generative AI (GenAI BA) with 6+ years of comprehensive business analysis experience to drive our enterprise cognitive solutions roadmap. In this high-visibility role, you will act as the pivotal bridge translating ambiguous executive business goals into actionable, production-ready AI capabilities.

You will partner closely with business leaders, domain Subject Matter Experts (SMEs), and technical AI engineering teams (GenAI Architects, Data Scientists, and Full-Stack ML Engineers) to discover high-value use cases, formulate detailed functional requirements for Retrieval-Augmented Generation (RAG) applications, define prompt strategies, establish benchmark evaluation rubrics, and orchestrate seamless User Acceptance Testing (UAT).

Key Responsibilities

C. Knowledge Base & Prompt Strategy Scoping

• Knowledge Base Curation: Collaborate with business SMEs and data engineers to identify, catalog, and curate internal enterprise knowledge bases, documents, and taxonomies for RAG indexing.
• RAG Data Structuring: Specify document chunking logic, metadata filtering schemas, and taxonomy tags to maximize semantic vector search retrieval accuracy.
• Prompt Specification: Define foundational prompt requirements: system instructions, tone-of-voice guidelines, persona parameters, few-shot examples, and output formatting templates.
• Guardrails & Ethics: Identify and document guardrail rules for PII/PHI redaction, copyright protection, brand safety, and hallucination containment.

D. AI Evaluation, UAT & Quality Benchmarking

• Ground-Truth Dataset Design: Construct comprehensive ground-truth test datasets containing representative real-world user queries, multi-turn dialogues, and complex edge cases.
• Evaluation Frameworks: Establish objective evaluation rubrics across core GenAI metrics: answer faithfulness, contextual relevance, answer completeness, tone alignment, and toxicity.
• UAT Execution: Orchestrate User Acceptance Testing (UAT) with business stakeholders; track, triage, and analyze model errors, hallucinations, and user feedback.
• Iterative Refinement: Conduct ongoing prompt iterations and parameter tuning alongside prompt engineers to continually refine response fidelity.

E. Stakeholder Enablement & Change Management

• Cross-Functional Liaison: Serve as the primary GenAI evangelist, bridging the communication divide between non-technical stakeholders and deep technical AI engineers.
• User Enablement & Training: Design training materials, playbooks, and prompt guides to accelerate internal end-user adoption and minimize prompt friction.
• Post-Launch Analytics: Monitor post-deployment analytics (user engagement, thumbs up/down feedback, deflection rates) to propose backlog enhancements.

A. AI Use Case Discovery & Feasibility Analysis

• Opportunity Identification: Lead discovery workshops with enterprise stakeholders to uncover workflow bottlenecks and evaluate potential GenAI automation opportunities.
• Feasibility & Solution Fit: Perform structured feasibility assessments comparing Generative AI / Foundation Models against traditional deterministic automation, rules engines, or predictive ML.
• ROI & Business Case Modeling: Develop comprehensive business cases, quantifying business impact, cost-benefit analyses, token expenditure estimates, and projected Return on Investment (ROI).
• Roadmap & Prioritization: Formulate Minimum Viable Product (MVP) roadmaps, prioritizing use cases based on business value, technical readiness, and risk tolerance.

B. Requirements Engineering for Probabilistic & AI Systems

• Functional Documentation: Author robust Business Requirement Documents (BRDs), Functional Specification Documents (FSDs), and Process Flow Diagrams (BPMN) for AI applications.
• Agile User Stories: Draft granular Agile User Stories with explicit Given-When-Then acceptance criteria specifically tailored to probabilistic AI behavior.
• Edge Cases & Fallbacks: Define system boundary specifications: intent classification thresholds, fallback mechanisms, out-of-domain query handling, and human-in-the-loop (HITL) escalation triggers.
• NFR Specification: Document Non-Functional Requirements (NFRs) including response latency thresholds, token rate limits, concurrency targets, and data residency constraints.

Skill Requirements

• Total Experience: Minimum 6+ years of professional IT Business Analysis experience, with at least 2+ years dedicated to AI/ML, NLP, Conversational AI, or GenAI products.
• Education: Bachelor's degree in Computer Science, Information Systems, Business Administration, Data Analytics, or related discipline.
• GenAI Domain Literacy: Solid conceptual grasp of Foundation Models (GPT-4, Claude, Gemini, Llama), RAG architectures, vector embeddings, semantic search, and agentic workflows.
• Agile & BA Tooling: Proficiency in Agile/Scrum delivery methodologies; expert administrator in Jira, Confluence, and process modeling tools (Visio, Miro, Lucidchart).
• Data & Querying Skills: Working proficiency with SQL for querying relational databases, validating test datasets, and analyzing feature performance.
• Experience Design: Experience defining user personas, customer journey maps, wireframes, and conversation dialogue trees for chatbots / AI assistants.

Communication: Outstanding executive presence, presentation prowess, and technical writing skills with a knack for simplifying complex AI concepts

Other Requirements

4. Preferred Certifications & Domain Knowledge

• Business Analysis Credentials: Certified Business Analysis Professional (CBAP), PMI Professional in Business Analysis (PMI-PBA), or Certified Scrum Product Owner (CSPO).
• Cloud & AI Credentials: AWS Certified AI Practitioner, Microsoft Certified: Azure AI Fundamentals (AI-900), or Azure AI Engineer Associate.
• Industry Verticals: Domain experience in highly regulated verticals: Banking & Financial Services, Insurance, Healthcare, Life Sciences, or Legal Tech.
• Regulatory Frameworks: Familiarity with enterprise AI governance frameworks (EU AI Act, NIST AI RMF, Responsible AI guidelines).

5. Key Performance Indicators (KPIs) & Success Metrics

The performance and effectiveness of the GenAI Business Analyst will be evaluated on the following key metrics:

• 1. User Adoption: Active User Adoption Rate: Driving 80% sustained end-user adoption and engagement for newly deployed GenAI capabilities.
• 2. Requirement Precision: Requirements Quality & Sprint Velocity: Maintaining requirement churn and sprint rework below 10% through precise acceptance criteria.
• 3. Delivery Velocity: Time-to-Value Delivery: Accelerating use case progression from initial discovery through PoC and pilot deployment within 6-8 weeks.
• 4. Quality & Faithfulness: AI Accuracy & Deflection Benchmark: Achieving 90% response faithfulness and acceptable UAT sign-off prior to production cutover.
• 5. Measurable ROI: Business Value Realization: Demonstrating quantifiable time savings, cost reductions, or revenue lift documented in post-launch reviews.

6. Candidate Evaluation Matrix (For Hiring Panels)

Interview panels should evaluate candidates across the following five core capability dimensions:

Evaluation Area

Competency Focus

Key Interview Signals

GenAI Problem Framing & Feasibility

Distinguishing high-ROI GenAI use cases from non-AI automation

Demonstrates clear logic on when to use RAG vs. fine-tuning vs. deterministic scripts; calculates token cost implications and business feasibility.

AI Requirements & Edge Case Definition

Handling non-deterministic outputs, fallbacks, and user stories

Articulates how to write acceptance criteria for probabilistic outputs; details clear escalation paths for low-confidence answers and hallucination traps.

RAG Knowledge Structuring & Prompting

Curation of unstructured data, metadata tags, and prompt specs

Explains how chunk size, document hierarchy, and metadata schemas impact retrieval accuracy; writes clear few-shot system prompts.

Testing, UAT & Evaluation Rubrics

Benchmarking faithfulness, relevance, and ground-truth validation

Details methodology for curating golden test datasets; explains how to grade AI outputs objectively with business SMEs during UAT.

Stakeholder Translation & Influence

De-hyping GenAI, managing expectations, and change enablement

Proven ability to bridge executive hype with technical reality; shares examples of overcoming end-user skepticism and driving adoption.

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Information at a Glance

Why HCLTech?

At HCLTech, youll supercharge your potential. Youll find your career. And youll find your spark. All at a place that knows that helping its customers stay on top starts by putting its people first.

HCLTech is a global technology company, home to more than 230,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending September 2026 totaled $14.8 billion.