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 current 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)

📌 Forward Deployement Engineer (Marunji)
🏢 Recognized
📍 Marunji