AI Solutions Analyst & Engineer

We are seeking a versatile and autonomous AI Solutions Analyst & Engineer to lead the delivery of Generative AI-powered initiatives , including the development of enterprise-grade chatbot solutions using Large Language Model (LLM) technologies .

In this hybrid role, you will combine business analysis, prompt engineering, and hands-on AI solution design to translate business needs into scalable AI solutions. You will work closely with internal AI experts, IT teams, and business stakeholders to drive projects from initial discovery through deployment and continuous improvement.

The successful candidate will play a key role in supporting digital transformation by helping implement and scale AI capabilities across the organization., AI Solution Delivery

• Lead the end-to-end delivery of Generative AI projects, including conversational AI and chatbot solutions.
• Design, develop, and refine LLM-based workflows such as prompt engineering and Retrieval-Augmented Generation (RAG) pipelines.
• Collaborate with internal AI and data teams to ensure solutions are robust, secure, and scalable.
• Translate business requirements into functional specifications and executable backlog items.

Business Analysis & Stakeholder Collaboration

• Work with business stakeholders to identify opportunities for AI-driven improvements and automation.
• Facilitate communication between technical and non-technical teams.
• Provide clear and structured explanations of AI models, including capabilities, limitations, and risk management.

Testing, Evaluation & Optimization

• Define and execute LLM testing strategies, including:
• Golden sets
• Regression tests
• Safety and adversarial testing
• Establish evaluation metrics such as accuracy proxies, groundedness, and response quality.
• Implement monitoring and logging practices for prompt/response traces, latency, cost, and quality signals.
• Drive continuous improvement based on feedback and performance insights.

Agile Delivery & Documentation

• Work within Agile environments, managing backlog items, user stories, and acceptance criteria.
• Coordinate and execute user testing and validation.
• Develop documentation, playbooks, training materials, and runbooks to support knowledge transfer and adoption.

• Structured, conscientious, and flexible approach to work
• Strong problem-solving and critical thinking abilities
• Excellent communication skills across technical and non-technical audiences
• Ability to deliver results under tight deadlines
• Collaborative team player mindset
• Curiosity and commitment to continuous learning
• Value-driven and solution-oriented mindset, * Experience combining Business Analysis, Digital Product Delivery, or Solution Engineering , including at least 2 years working with GenAI / LLM-based solutions
• Demonstrated experience delivering conversational AI or chatbot solutions end-to-end (discovery * build * test * rollout)
• Proven ability to design and iterate prompts and RAG pipelines, d evelop evaluation frameworks and c ollaborate with AI, data, and architecture teams
• Hands-on experience translating business requirements into functional specifications and backlog items
• Experience defining and executing LLM testing strategies
• Comfortable working in Agile delivery environments with autonomy to plan and execute projects

Preferred

• Experience in the insurance or financial services industry
• Background in data science

Technical Skills

• Microsoft Office 365
• Microsoft Copilot M365
• Data and reporting tools (Power BI, Azure Databricks, Azure Data Lake)
• Programming languages (Python, SQL)
• Experience with LLM and Generative AI solutions

Functional Experience (5-8 years of experience)

• Understanding of operational workflows and constraints to design practical solutions
• Agile delivery practices (backlog management, user stories, acceptance criteria)
• Test strategy design and coordination of user testing activities
• Experience implementing:
• LLM evaluation frameworks
• Monitoring and logging for AI solutions
• Documentation and knowledge transfer processes