> Markdown version of [/jobs/ext/4505665-sr-solution-architect-_-generative-ai](https://www.wearedevelopers.com/jobs/ext/4505665-sr-solution-architect-_-generative-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr. Solution Architect _ Generative Ai

- **Company:** Tata Consultancy Services Limited
- **Location:** Addison, TX, United States
- **Experience:** Expert
- **Salary:** $100,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Audit Trail, Cloud Engineering, Databases, Continuous Integration, Data Mining, Software Design Documents, Distributed Systems, Elasticsearch, Information Extraction, Information Retrieval, Python (Programming Language), Performance Tuning, OpenAI, Search Technologies, Management of Software Versions, Pinecone, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Llamaindex, Generative AI, Fastapi, Information Technology, Deployment Automation, HuggingFace, Machine Learning Operations, FAISS, Model Inference, Document Classification, Intelligent Document Processing, Microservices
- **Published:** November 2, 2026
- **Apply:** https://diversityjobs.com/career/18814190/Sr-Solution-Architect-_-Generative-Ai-Texas-Addison

## About the Role

Must Have Technical/Functional Skills
Primary Skill: Data science, architecture, genAI architecture
Secondary: Python, LLM, AI/ML, Vector search tech(Elasticsearch, pinecone, FAISS)
Experience: 8 to 10 years, * 8+ years of experience in software or solution architecture

 * 3+ years of experience in AI/ML or Generative AI
 * Strong hands-on expertise with:
o LLMs and GenAI frameworks (OpenAI, Hugging Face, LLaMA, Mistral)
o Embeddings and vector search technologies (Elasticsearch, Pinecone, FAISS)

 * Advanced proficiency in Python, including:
o FastAPI
o LangChain / LlamaIndex

 * Proven experience designing and implementing RAG-based solutions at enterprise scale
 * Experience with:
o GPU-based model serving and optimization (vLLM, Triton)
o Cloud-native architectures and microservices
o MLOps practices (CI/CD, monitoring, versioning)

 * Strong understanding of risk, compliance, and governance frameworks in regulated environments
 * Excellent communication skills, with the ability to present comple x technical concepts to executive stakeholders
Preferred Qualifications

 * Master's degree in computer science, Data Science, or related field
 * Experience delivering Document AI or correspondence processing platforms
 * Demonstrated ability to:
o Lead architecture from concept through production
o Operate independently with strong ownership

 * Strong problem-solving and analytical capabilities
 * Experience navigating enterprise data ecosystems and cross-LOB collaboration
 * Ability to manage multiple priorities in a fast-paced environment Work Model, BACHELOR OF COMPUTER SCIENCE

## Description

The DCRS Inbound Correspondence Team is seeking a Senior Generative AI Solutions Architect to lead the design and scaling of an enterprise-grade Document Intelligence platform within the DCRS Inbound Correspondence organization.
This role will define architecture for LLM-powered solutions that enable document classification, intelligent data extraction, and retrieval-augmented generation (RAG) across large, complex document ecosystems.
The ideal candidate combines deep expertise in GenAI, distributed systems, and enterprise architecture, with a strong focus on performance, governance, and regulatory compliance.
Key Responsibilities

 * Architect and deliver scalable GenAI solutions for:
o Document classification and routing
o Data extraction and entity recognition
o Knowledge retrieval and Q&A using RAG

 * Design and optimize end-to-end RAG architectures, including:
o Embeddings and chunking strategies
o Vector database selection and tuning
o Retrieval, re-ranking, and response generation

 * Lead deployment strategies for LLM inference at scale, partnering with infrastructure teams on:
o GPU-based environments (vLLM, Triton)
o Performance tuning and cost optimization

 * Establish and implement model governance frameworks, including:
o Explainability and traceability
o Audit trails and compliance alignment
o Evaluation pipelines and model benchmarking

 * Define reference architectures, standards, and best practices for GenAI adoption across the enterprise
 * Produce architecture artifacts:
o Diagrams, design documents, and technical roadmaps
o Executive-level presentations and decision materials

 * Collaborate across Engineering, Product, Risk, and Compliance to ensure safe, scalable, and business-aligned solutions