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.

Roles and 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 the 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 throughout 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 approaches.
- 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 a deep understanding of client business domains and industry-specific challenges.
- Research industry trends, competitive landscapes, and emerging AI use cases.
- Identify opportunities for AI-driven innovation and business transformation.
- Build reusable frameworks, templates, and best practices for AI 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 such as MLflow, Kubeflow, Vertex AI, SageMaker, and Azure ML.
- Knowledge of model deployment strategies including batch, real-time, and streaming.
- Experience with containerization technologies such as Docker and Kubernetes.
- Experience with cloud platforms including GCP, AWS, and 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.
- Solid foundation in supervised and unsupervised learning algorithms.
- Experience with deep learning frameworks such as TensorFlow, PyTorch, and JAX.
- Understanding of model development, training, evaluation, and optimization.

Nice-to-Have Skills Specialized AI Platforms & Tools

- Experience with Google Cloud AI Platform:

- Vertex AI

- Document AI

- Dialogflow CX
- Experience with AWS AI Services:

- Bedrock

- SageMaker

- Comprehend

- Kendra
- Experience with Azure AI Services:

- Azure OpenAI Service

- Cognitive Services

- Azure ML Studio
- Experience with open-source AI frameworks:

- Hugging Face

- LangChain

- LlamaIndex

Domain & Industry Expertise

- Domain expertise in specific industries such as:

- Financial Services

- Healthcare

- Retail

- Manufacturing

Responsible AI & Governance

- Experience with responsible AI frameworks.
- Knowledge of model explainability techniques such as SHAP and LIME.
- Experience with bias detection.
- Experience with AI governance, compliance frameworks, and regulatory requirements.

Emerging AI Technologies

- Multimodal AI, including vision-language models and audio processing.
- Reinforcement Learning from Human Feedback (RLHF).
- Constitutional AI and AI alignment.
- Edge AI and model optimization techniques.
- Agentic workflows and autonomous systems.
- Compound AI systems.
- Small Language Models (SLMs) and model distillation.
- AI-powered automation and intelligent process automation.

Certifications

- GCP Professional Machine Learning Engineer.
- AWS Machine Learning Specialty.
- Azure AI Engineer.

Sales & Pre-Sales Experience

- Experience in critical phases of the sales process, including:

- Requirement gathering

- Sales planning

- Solution scoping

- Proposal writing

- Presentation and presales activities

📌 Business Analyst (Bengaluru)
🏢 Quantiphi Analytics Solutions
📍 Bengaluru