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)