Forward Deployement Engineer

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

Experience with one or more:

• LangChain
  
• LangGraph
  
• Semantic Kernel
  
• AutoGen
  
• CrewAI
  
• Prompt Engineering and Evaluation Frameworks
  
• Vector Databases (Pinecone Azure AI Search Weaviate ChromaDB)