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)