Senior Business Analyst

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 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