DE&A - AIML - Data Science - Artificial Intelligence of Things (AIOT)

Design and develop enterprise RAG applications using LLMs, embeddings, vector databases, and hybrid search.

Build end-to-end document ingestion and knowledge ingestion pipelines for structured and unstructured data.

Implement document parsing, chunking, metadata enrichment, embeddings, indexing, and retrieval strategies.

Design and optimize semantic, vector, keyword, and hybrid search solutions.

Develop RAG workflows incorporating query understanding, query rewriting, retrieval, reranking, context generation, and response generation .

Work with LLMs such as Azure OpenAI, Anthropic Claude, or equivalent models .

Develop agentic AI solutions using tools, function calling, MCP, and multi-agent/single-agent architectures where appropriate.

Implement RAG evaluation and observability including retrieval quality, answer relevance, groundedness, hallucination detection, latency, and token/cost monitoring.

Optimize RAG applications for accuracy, latency, scalability, and cost .

Integrate RAG applications with enterprise systems, APIs, databases, repositories, and knowledge sources.

Develop secure APIs and backend services for AI applications.

Collaborate with architects, developers, business analysts, and domain experts to translate business requirements into AI solutions.

Establish best practices around prompt engineering, context management, guardrails, security, and responsible AI .

Troubleshoot production issues and continuously improve the AI application based on user feedback and evaluation metrics.

Required Technical Skills

Generative AI / LLM

Strong understanding of LLMs and Generative AI

Prompt engineering and structured prompting

LLM inference and model selection

Function calling / tool calling

Context-window management

Understanding of hallucination and grounding challenges

RAG

Strong hands-on experience building RAG applications

Document ingestion and preprocessing

Chunking strategies

Metadata design and filtering

Embedding generation

Vector search

Hybrid search

Reranking

Query expansion / rewriting

Retrieval optimization

RAG evaluation

AI / Agentic Frameworks

Experience with one or more frameworks such as:

LangGraph

Google ADK

Experience with MCP (Model Context Protocol) is a plus.

Understanding of agent orchestration and tool-based workflows.

Cloud & Search

Strong experience with Microsoft Azure

Azure OpenAI / Azure AI Foundry

Azure AI Search or equivalent vector search platform

Azure Blob Storage

Azure App Service / Functions

API Management

Experience with AWS AI services or Amazon OpenSearch is a plus.

Programming

Strong Python development skills

REST API development

Flask / FastAPI

JSON and API integrations

Experience with SQL and relational databases

Databases / Search

Vector databases/search engines such as:

Azure AI Search

OpenSearch

PostgreSQL/pgvector

Pinecone

Elasticsearch

Weaviate

Understanding of indexing and search optimization.

RAG Evaluation & Observability

Experience with AI observability and evaluation tools such as:

Arize Phoenix

LangSmith

Azure AI evaluation capabilities

RAGAS

Custom evaluation frameworks

Knowledge of metrics such as:

Context relevance

Context precision/recall

Answer relevance

Faithfulness / groundedness

Retrieval accuracy

Hallucination rate

Latency

Token consumption

Cost per request

Preferred / Good-to-Have Skills

Experience with Guidewire PolicyCenter, ClaimCenter, BillingCenter , or other enterprise insurance platforms.

Experience working with large technical documentation repositories.

Understanding of Guidewire data models, APIs, configuration, and data dictionaries.

Experience building AI assistants for enterprise developers.

Experience with structured knowledge extraction from HTML, XML, JSON, PDFs, source code, database schemas, and technical documentation .

Knowledge of enterprise security, RBAC, PII protection, and data governance.

Experience with semantic caching and

.