About this role
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)