Senior GenAI/Python Engineer

Tata Consultancy Services; Chicago (IL); On-site; USD 90,000 - 130,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Medical Coverage

401K Plan

Job summary

Tata Consultancy Services in Chicago seeks an experienced Python developer to architect and ship GenAI-powered agentic applications. You will design domain-specific agent workflows, build JSON payload pipelines, and integrate semantic search with vector stores to deliver accurate extractions.

The role emphasizes FastAPI-based microservices, async orchestration, and rigorous validation, with collaboration across product, data, and engineering teams. Salary range reflects senior-level expertise.

Qualifications

• 5+ years of advanced Python development with production-grade practices.
• Hands-on with GenAI/LLM apps in production using model APIs.
• Experience building LLM-powered agents and multi-step orchestration.
• Experience with agentic AI frameworks or patterns like LangChain.
• Strong JSON schema validation, retry/repair, and data extraction workflows.

Responsibilities

• Build and enhance LLM-powered agentic applications using Python.
• Develop intent classification and domain-specialist agent workflows.
• Create JSON payload pipelines from user utterances with filters and rankings.
• Integrate semantic search and vector retrieval for entity resolution.
• Design, build, and consume FastAPI-based microservices.
• Implement async orchestration for parallel LLM/API calls.
• Develop prompts, validation, function calling, and guardrails.
• Create evaluation harnesses and regression tests for extraction accuracy.
• Collaborate across platform, data, service, and product teams.
• Document architecture with Confluence, ADRs, and diagrams.

Skills

Python development

GenAI / LLM collaboration

Communication

SQL analysis

Cloud experience

Async programming

API design

Code reviews

Tools

Docker

Git

CI/CD

FastAPI

LangChain

Pydantic

Azure/OpenAI APIs

Job description

• 5+ years of advanced professional Python development experience with production-grade coding practices.
• Strong hands-on experience writing clean, tested, maintainable code using typing, pytest, packaging standards, Git, CI/CD, Docker, and code review discipline.
• 1.5+ years of hands-on experience building GenAI / LLM applications in production using OpenAI, Azure OpenAI, Anthropic, Bedrock, or similar model APIs.
• Experience building LLM-powered agents, including intent classification, domain-specific agents, agentic extraction flows, and multi-step orchestration.
• Hands-on experience with Agentic AI frameworks or patterns such as LangChain, LangGraph, LlamaIndex, function calling, tool use, or custom orchestration.
• Strong experience in structured output extraction from LLMs using JSON schema enforcement, Pydantic, retry/repair strategies, and validation logic.
• Experience with RAG and vector search concepts including embeddings, chunking, hybrid search, reranking, entity resolution, fuzzy matching, confidence thresholds, and disambiguation flows.
• Working knowledge of vector databases or search platforms such as pgvector, Pinecone, Weaviate, OpenSearch, Snowflake vector functions, or equivalent.
• Strong API development experience using FastAPI or similar frameworks such as Flask or Django.
• Experience with async Python and orchestration of parallel LLM/API calls.
• Strong SQL skills and comfort working with large analytical datasets.
• Cloud environment experience, preferably Azure; AWS or GCP acceptable.
• Strong written and verbal communication skills, with ability to collaborate directly with engineering teams, product owners, and cross-functional stakeholders.
• Build and enhance LLM-powered agentic applications using Python.
• Develop intent classification, domain-specialist agent workflows, and agentic extraction pipelines for complex user questions.
• Build natural-language-to-structured-payload pipelines that convert user utterances into accurate JSON query payloads, including filters, exclusions, rankings, and metric selection.
• Integrate semantic search and vector retrieval services for embedding-based entity resolution, fuzzy matching, confidence scoring, and disambiguation flows.
• Design, build, and consume FastAPI-based microservices.
• Implement async orchestration patterns for parallel LLM calls, API calls, and downstream service integrations.
• Develop prompt engineering patterns, structured output enforcement, JSON schema validation, function calling, tool usage, and guardrails.
• Build evaluation harnesses, test sets, and regression test frameworks to measure extraction accuracy and validate prompt/model changes.
• Work with metadata/catalog services, entitlement-aware data access, and reporting-engine payload contracts.
• Collaborate with multiple platform, data, service, and product teams to deliver production-ready GenAI capabilities.
• Write clear technical documentation including Confluence pages, ADRs, sequence diagrams, and flow diagrams.
• Participate in hands-on technical evaluation, code reviews, design discussions, and production readiness reviews.

Job Description

Must Have Technical/Functional Skills

• 5+ years of advanced professional Python development experience with production-grade coding practices.
• Strong hands-on experience writing clean, tested, maintainable code using typing, pytest, packaging standards, Git, CI/CD, Docker, and code review discipline.
• 1.5+ years of hands-on experience building GenAI / LLM applications in production using OpenAI, Azure OpenAI, Anthropic, Bedrock, or similar model APIs.
• Experience building LLM-powered agents, including intent classification, domain-specific agents, agentic extraction flows, and multi-step orchestration.
• Hands-on experience with Agentic AI frameworks or patterns such as LangChain, LangGraph, LlamaIndex, function calling, tool use, or custom orchestration.
• Strong experience in structured output extraction from LLMs using JSON schema enforcement, Pydantic, retry/repair strategies, and validation logic.
• Experience with RAG and vector search concepts including embeddings, chunking, hybrid search, reranking, entity resolution, fuzzy matching, confidence thresholds, and disambiguation flows.
• Working knowledge of vector databases or search platforms such as pgvector, Pinecone, Weaviate, OpenSearch, Snowflake vector functions, or equivalent.
• Strong API development experience using FastAPI or similar frameworks such as Flask or Django.
• Experience with async Python and orchestration of parallel LLM/API calls.
• Strong SQL skills and comfort working with large analytical datasets.
• Cloud environment experience, preferably Azure; AWS or GCP acceptable.
• Strong written and verbal communication skills, with ability to collaborate directly with engineering teams, product owners, and cross-functional stakeholders.

Roles & Responsibilities

• Build and enhance LLM-powered agentic applications using Python.
• Develop intent classification, domain-specialist agent workflows, and agentic extraction pipelines for complex user questions.
• Build natural-language-to-structured-payload pipelines that convert user utterances into accurate JSON query payloads, including filters, exclusions, rankings, and metric selection.
• Integrate semantic search and vector retrieval services for embedding-based entity resolution, fuzzy matching, confidence scoring, and disambiguation flows.
• Design, build, and consume FastAPI-based microservices.
• Implement async orchestration patterns for parallel LLM calls, API calls, and downstream service integrations.
• Develop prompt engineering patterns, structured output enforcement, JSON schema validation, function calling, tool usage, and guardrails.
• Build evaluation harnesses, test sets, and regression test frameworks to measure extraction accuracy and validate prompt/model changes.
• Work with metadata/catalog services, entitlement-aware data access, and reporting-engine payload contracts.
• Collaborate with multiple platform, data, service, and product teams to deliver production-ready GenAI capabilities.
• Write clear technical documentation including Confluence pages, ADRs, sequence diagrams, and flow diagrams.
• Participate in hands-on technical evaluation, code reviews, design discussions, and production readiness reviews.

Salary Range-$90,000-$130,000 a year

TCS Employee Benefits Summary

• Discretionary Annual Incen tive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.