Solution Architect AI
Tata Consultancy Services
•
Tampa, FL
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0
0
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Not Applicable
Posted April 17, 2026
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Responsibilities
Responsibilities
- Design and architect scalable, maintainable AI solutions that integrate Langchain, LangGraph, and RAG
- methodologies to enhance knowledge discovery and conversational AI capabilities.
- Lead development efforts using Python to prototype and productionize AI agents and workflows.
- Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment
- strategies.
- Develop and optimize RAG systems combining vector search, knowledge graphs, and LLMs to provide
- contextual and accurate responses.
- Architect Agentic AI systems that perform autonomous tasks by chaining actions, managing states, and
- integrating external APIs.
- Provide technical leadership and architecture guidance for AI and NLP projects.
- Evaluate emerging AI technologies and frameworks to continuously improve solution design.
- Create comprehensive technical documentation and architecture diagrams to facilitate knowledge transfer.
- Ensure solutions meet security, compliance, and performance standards.
Not Met Priorities
What still needs stronger evidence
Requirements
- Python, Langchain, LangGraph, Retrieval-Augmented Generation (RAG), and Agentic AI
- Create comprehensive technical documentation and architecture diagrams to facilitate knowledge transfer.
- Ensure solutions meet security, compliance, and performance standards.
- Strong proficiency in Python, with experience in backend or AI service development.
- Hands-on experience with Langchain and/or LangGraph frameworks.
- Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.
- Experience designing and implementing agentic AI architectures, autonomous workflows, or multi-agent systems.
- Familiarity with knowledge graphs, vector search engines (e.g., FAISS, Pinecone, Weaviate), and LLM integration.
- Solid understanding of NLP concepts, transformer models, and prompt engineering.
- Ability to translate complex business requirements into scalable AI solutions.
- Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
- Strong communication skills and ability to collaborate across multidisciplinary teams.
Job Description
Job Summary:
As a Solution Architect with a focus on AI-driven intelligent systems, you will lead the design, development, and
deployment of advanced NLP and cognitive search solutions using Python, Langchain, LangGraph,
Retrieval-Augmented Generation (RAG), and Agentic AI models.
Must Have Technical/Functional Skills
Python, Langchain, LangGraph, Retrieval-Augmented Generation (RAG), and Agentic AI
Key Responsibilities
Design and architect scalable, maintainable AI solutions that integrate Langchain, LangGraph, and RAG
methodologies to enhance knowledge discovery and conversational AI capabilities.
Lead development efforts using Python to prototype and productionize AI agents and workflows.
Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment
strategies.
Develop and optimize RAG systems combining vector search, knowledge graphs, and LLMs to provide
contextual and accurate responses.
Architect Agentic AI systems that perform autonomous tasks by chaining actions, managing states, and
integrating external APIs.
Provide technical leadership and architecture guidance for AI and NLP projects.
Evaluate emerging AI technologies and frameworks to continuously improve solution design.
Create comprehensive technical documentation and architecture diagrams to facilitate knowledge transfer.
Ensure solutions meet security, compliance, and performance standards.
Required Skills And Q Ualifications
Strong proficiency in Python, with experience in backend or AI service development.
Hands-on experience with Langchain and/or LangGraph frameworks.
Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.
Experience designing and implementing agentic AI architectures, autonomous workflows, or multi-agent systems.
Familiarity with knowledge graphs, vector search engines (e.g., FAISS, Pinecone, Weaviate), and LLM integration.
Solid understanding of NLP concepts, transformer models, and prompt engineering.
Ability to translate complex business requirements into scalable AI solutions.
Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
Strong communication skills and ability to collaborate across multidisciplinary teams.
Salary Range: $38,000 - $120,000 a year
Job Summary:
As a Solution Architect with a focus on AI-driven intelligent systems, you will lead the design, development, and
deployment of advanced NLP and cognitive search solutions using Python, Langchain, LangGraph,
Retrieval-Augmented Generation (RAG), and Agentic AI models.
Must Have Technical/Functional Skills
Python, Langchain, LangGraph, Retrieval-Augmented Generation (RAG), and Agentic AI
Key Responsibilities
Design and architect scalable, maintainable AI solutions that integrate Langchain, LangGraph, and RAG
methodologies to enhance knowledge discovery and conversational AI capabilities.
Lead development efforts using Python to prototype and productionize AI agents and workflows.
Collaborate with data engineering and DevOps teams to implement data pipelines and model deployment
strategies.
Develop and optimize RAG systems combining vector search, knowledge graphs, and LLMs to provide
contextual and accurate responses.
Architect Agentic AI systems that perform autonomous tasks by chaining actions, managing states, and
integrating external APIs.
Provide technical leadership and architecture guidance for AI and NLP projects.
Evaluate emerging AI technologies and frameworks to continuously improve solution design.
Create comprehensive technical documentation and architecture diagrams to facilitate knowledge transfer.
Ensure solutions meet security, compliance, and performance standards.
Required Skills And Q Ualifications
Strong proficiency in Python, with experience in backend or AI service development.
Hands-on experience with Langchain and/or LangGraph frameworks.
Deep understanding of Retrieval-Augmented Generation (RAG) systems and techniques.
Experience designing and implementing agentic AI architectures, autonomous workflows, or multi-agent systems.
Familiarity with knowledge graphs, vector search engines (e.g., FAISS, Pinecone, Weaviate), and LLM integration.
Solid understanding of NLP concepts, transformer models, and prompt engineering.
Ability to translate complex business requirements into scalable AI solutions.
Experience with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
Strong communication skills and ability to collaborate across multidisciplinary teams.
Salary Range: $38,000 - $120,000 a year