Forward Deployement Engineer

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 modern 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)