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

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 agile 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

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