Key Responsibilities

Business Discovery & Solution Design

- Partner with business leaders, product owners, and operational teams to identify high-value AI use cases.

- Conduct workshops and discovery sessions to understand workflows, pain points, and business objectives.

- Translate business requirements into scalable AI and automation solutions.

- Define MVP scope, success criteria, KPIs, and implementation roadmaps.

AI Engineering & Development

- Design, build, and deploy Generative AI and Agentic AI solutions.

- Develop RAG (Retrieval Augmented Generation) applications leveraging enterprise knowledge sources.

- Build intelligent agents capable of automating underwriting, claims, customer service, IT support, and operational workflows.

- Integrate AI services with enterprise platforms, APIs, databases, SharePoint, ServiceNow, CRM, and document repositories.

Platform Integration & Deployment

- Deploy AI models and applications into Azure cloud environments.

- Build secure and compliant integrations aligned with enterprise governance standards.

- Configure monitoring, observability, logging, and performance metrics.

- Support production deployment and operational readiness activities.

Production Ownership

- Own the end-to-end success of deployed AI solutions.

- Troubleshoot production issues and optimize model performance.

- Improve solution accuracy, latency, scalability, reliability, and cost efficiency.

- Establish feedback mechanisms and continuous improvement processes.

Stakeholder Engagement





- Collaborate with business executives, architects, developers, data engineers, and security teams.

- Present solution architectures, progress updates, and business value realization metrics.

- Facilitate adoption and change management activities.

- Mentor internal teams on AI engineering best practices.

Innovation & Value Creation

- Continuously identify new AI opportunities within underwriting, claims, risk management, customer service, and corporate operations.

- Prototype emerging AI capabilities and demonstrate proof-of-value.

- Recommend reusable AI assets, frameworks, and accelerators.

- Support strategic AI roadmap development and future-state architecture.

Required Qualifications

Technical Skills

- Strong proficiency in Python and contemporary software engineering practices.

- Hands-on experience with Generative AI technologies, LLMs, and AI agents.

- Experience building RAG pipelines using vector databases and enterprise content repositories.

- Strong knowledge of Azure AI services, Azure OpenAI, Azure Functions, and cloud-native development.

- Experience with REST APIs, microservices, containers, and CI/CD pipelines.

- Familiarity with model deployment, monitoring, evaluation frameworks, and MLOps practices.

AI & Agent Frameworks

Experience with one or more:

- LangChain

- LangGraph

- Semantic Kernel

- AutoGen

- CrewAI

- Prompt Engineering and Evaluation Frameworks

- Vector Databases (Pinecone, Azure AI Search, Weaviate, ChromaDB)

📌 Forward Deployement Engineer (Haveli)
🏢 Fulcrum Digital
📍 Haveli