Senior Business Analyst

AEI SBA - Agentic Enterprise Intelligence
Role: Senior Business Analyst Agentic Enterprise Intelligence (AEI)
Work Location: India (Mumbai, Bangalore)
Experience: 5+ years
Job Description
As part of the core delivery team of Quantiphi, the Senior Business Analyst for AEI will be
responsible for bridging the gap between business requirements and technical
implementation for enterprise AI solutions. Your focus will be to translate complex
business problems into actionable AI use cases, design solution architectures for
Generative AI, Custom ML, Document AI, and Agentic AI projects, and ensure successful
delivery through effective stakeholder management and requirements analysis.
You will act as a conduit between technical team members (ML engineers, platform
engineers, software developers etc.) and business customers, taking the lead in
preparing functional and technical specification documents for AI-driven solutions.
Key Responsibilities
Requirements Gathering & Analysis:






Gather customer requirements and analyze their needs to determine analytical
and AI solution requirements for enterprise projects
Define comprehensive use cases for Generative AI, Agentic AI, Document AI, and
Custom ML implementations
Conduct discovery workshops and stakeholder interviews to identify AI
opportunities and business value drivers
Translate business objectives into measurable KPIs and success metrics for AI
initiatives
Document detailed functional and technical requirements, user stories, and
acceptance criteria
Perform gap analysis between current state and desired AI-enabled future state

Create detailed process flow diagrams, data flow diagrams, and system
architecture documentation
Solution Design & Architecture:







Design end-to-end AI solution architectures aligned with business requirements
and technical constraints
Define data requirements, model specifications, and integration touchpoints for AI
systems
Collaborate with ML architects and engineers to design appropriate model
architectures and training strategies
Create solution blueprints for RAG (Retrieval-Augmented Generation) systems,
agentic workflows, and document processing pipelines
Design user interaction flows for AI-powered applications and intelligent
automation solutions
Define data governance, model governance, and responsible AI frameworks for
implementations
Develop proof-of-concept specifications and MVP scopes for AI initiatives
Stakeholder Management & Communication:







Act as a liaison between stakeholders, project teams, development teams, and
data science teams across the project lifecycle
Manage customer communication and relationships across business and
technical stakeholders
Present solution designs, project updates, and business value propositions to
C-suite executives and business leaders
Facilitate workshops, design thinking sessions, and collaborative problem-solving
meetings
Guide customers on AI technology evaluation, use case prioritization, and ROI
analysis
Translate complex AI concepts into business-friendly language for non-technical
audiences
Manage expectations and negotiate scope, timelines, and deliverables with
stakeholders

Project Execution & Delivery:



Apply industry best practices and promote standards for execution and delivery
approach
Showcase thought leadership on AI technology roadmaps, agile development
methodologies, and best practices
Create and maintain project documentation including BRDs, FRDs, user stories,
and test cases


Support sprint planning, backlog grooming, and release planning activities
Coordinate UAT (User Acceptance Testing) and gather feedback for iterative
improvements


Track project metrics, identify risks, and propose mitigation strategies
Ensure alignment between business requirements and technical deliverables
throughout the project lifecycle

Support change management and user adoption activities for AI solutions
Domain & Industry Expertise:

Develop deep understanding of client business domains and industry-specific
challenges



Research industry trends, competitive landscape, and emerging AI use cases
Identify opportunities for AI-driven innovation and business transformation
Build reusable frameworks, templates, and best practices for AEI solution delivery
Required Skills

Excellent communication, articulation, abstraction, analytical, and
presentation skills

Ability to work with minimal supervision in a dynamic and time-sensitive work
environment


Team management experience is a must
Excellent aptitude in business analysis and awareness of quantitative analysis
techniques


Strong stakeholder management and client-facing skills
Hands-on experience with Large Language Models (LLMs) such as GPT-4,
Claude, Gemini, Llama, or similar foundation models

Proficiency in prompt engineering, fine-tuning, and RAG (Retrieval-Augmented
Generation) architectures
Hands-on experience with MLOps tools and practices (MLflow, Kubeflow, Vertex
AI, SageMaker, Azure ML)


Knowledge of model deployment strategies (batch, real-time, streaming)
Experience with containerization (Docker, Kubernetes) and cloud platforms
(GCP, AWS, Azure)




Understanding of model monitoring, drift detection, and retraining pipelines
Knowledge of API design and microservices architecture
Familiarity with data governance, privacy, and security best practices
Experience designing and implementing AI agents with autonomous
decision-making capabilities

Knowledge of multi-agent systems, agent orchestration, and tool-use
patterns



Strong foundation in supervised and unsupervised learning algorithms
Experience with deep learning frameworks (TensorFlow, PyTorch, JAX)
Understanding of model development, training, evaluation, and optimization
Nice to Have Skills

Experience with specialized AI platforms and tools:




Google Cloud AI Platform (Vertex AI, Document AI, Dialogflow CX)
AWS AI Services (Bedrock, SageMaker, Comprehend, Kendra)
Azure AI Services (OpenAI Service, Cognitive Services, ML Studio)
Open-source AI frameworks (Hugging Face, LangChain, LlamaIndex)



Domain expertise in specific industries (Financial Services, Healthcare, Retail,
Manufacturing)
Experience with responsible AI frameworks, model explainability (SHAP, LIME),
and bias detection
Knowledge of emerging AI trends:




Multimodal AI (vision-language models, audio processing)
Reinforcement Learning from Human Feedback (RLHF)
Constitutional AI and AI alignment
Edge AI and model optimization techniques

Certifications in cloud platforms (GCP Professional ML Engineer, AWS ML
Specialty, Azure AI Engineer)
Experience with AI governance, compliance frameworks, and regulatory
requirements
Knowledge and willingness to learn and apply emerging trends in:




Agentic workflows and autonomous systems
Compound AI systems
Small Language Models (SLMs) and model distillation
AI-powered automation and intelligent process automation

Experience in critical phases of the sales process including requirement
gathering, sales planning, solution scoping, proposal writing, and presentation