- Serve as the principal liaison between business stakeholders and technical development teams, specializing in the implementation and enhancement of enterprise-grade Conversational AI solutions, including chatbots and applications powered by Large Language Models (LLMs).

- Drive the end-to-end project lifecycle for technical initiatives, focusing on requirements gathering, detailed documentation, user story creation, and validation of AI/ML model efficacy and business value.

- Mandate adherence to the UK shift (12 :30 PM to 9 :30 PM IST) to ensure effective collaboration with European teams and operate under a hybrid work model requiring presence at client locations in Bangalore or Pune.

- Leverage a minimum of 6+ years of specialized experience in Business Analysis for technology implementation projects, with a strong focus on AI/ML applications.

Core Responsibilities :

Technical Requirements Engineering and Documentation :

- Lead Business Analysis activities for complex, high-stakes technical projects, ensuring stringent alignment between functional specifications and overarching organizational and technical strategy.

- Conduct in-depth requirements elicitation sessions, precisely translating ambiguous business needs into verifiable, implementable functional and non-functional specifications for NLU and generative AI systems.

- Produce rigorous documentation artifacts, including detailed Business Requirements Documents (BRDs), Functional Specification Documents (FSDs), detailed user stories (following the INVEST principles), and system traceability matrices.

- Own and continuously manage the product backlog for Conversational AI streams, actively collaborating with Product Owners to prioritize features based on technical feasibility, risk, and maximum business value.

Conversational AI and LLM Technical Implementation :

- Demonstrate practical, full-lifecycle experience in implementing enterprise-scale Chatbot solutions, including defining NLU model training sets, designing complex dialogue flow/state machines, and specifying API integrations with core backend systems (e.g., CRM, ERP).

- Possess direct, hands-on experience in projects utilizing Large Language Models (LLMs), including defining prompting strategies, configuring Retrieval-Augmented Generation (RAG) architectures, and setting up testing frameworks for generative outputs (e.g., measuring hallucination rate, relevance, and safety).

- Demonstrate practical knowledge of Google AI offerings, including advanced configuration of Dialogflow (ES/CX), utilization of the Vertex AI platform for model deployment, and working with relevant LLM APIs (PaLM, Gemini).

- Define and implement technical KPIs and metrics for AI solutions (e.g., technical containment rate, NLU accuracy, intent confidence scores) and establish continuous feedback loops for model retraining and performance optimization.

Stakeholder Management and Delivery :

- Organize and moderate interactive, outcomes-driven technical workshops and design sprints with both local and global clients to finalize architecture, define solution scope, and obtain sign-off on technical designs.

- Expertly adapt communication to articulate complex AI concepts and technical debt to non-technical business leaders, while also providing clear, actionable specifications to development and data science teams.

- Lead the User Acceptance Testing (UAT) phase for integrated AI features, designing test cases, coordinating user groups, collecting detailed feedback, and ensuring the technical solution rigorously meets all functional requirements.

- Utilize tools and methodologies for process modeling and visual representation of complex, multi-turn conversational flows and system integrations.

Required Technical Skills and Experience :

- Minimum of 6+ years of experience in Business Analysis, with a strong, dedicated focus on technology implementation, particularly within the AI/ML domain.

- Strong technical understanding of API specifications (e.g., RESTful, JSON payloads) and common system integration patterns for linking Conversational AI services to enterprise data layers.

- Proficiency with Agile frameworks (Scrum/Kanban) and project management tools used for technical development (Jira, Confluence).

- Ability to work the mandatory shift timing and adhere to the hybrid work model.

Preferred Skills and Certifications :

- Professional certification in Business Analysis (CBAP, CCBA, or relevant industry equivalent).

- Basic proficiency in scripting or data manipulation languages (Python, SQL) for performing preliminary analysis on conversational logs, defining model training data, or validating output data schemas.

- Knowledge of Responsible AI principles, including practical experience with techniques for bias detection, fairness assessment, and safety guardrails in LLM deployment.

- Experience with cloud security protocols and governance related to sensitive data processed by AI services.