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

Key Responsibilities

• 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 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

.