Technical Lead

Tata Consultancy Services; Cary (NC); On-site; USD 110,000 - 130,000; Full time

Job summary

Tata Consultancy Services in Cary, NC is seeking a senior software engineer to architect and productionize cloud native backend services and AI/LLM inference pipelines.

You will design Python-based APIs and microservices with FastAPI, implement LLM capabilities, and ensure observability, CI/CD, and scalable deployments using Kubernetes and Helm. This role emphasizes performance, reliability, and collaboration with frontend teams.

Qualifications

• Experience building cloud native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns).
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.

Responsibilities

• Build and productionize cloud native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
• Build event driven, resilient integrations and containerized services, with hands‑on Kubernetes debugging and Helm-based deployments.
• Establish observability, SLOs, CI/CD automation, testing.

Skills

Cloud native backends

Python APIs

LangChain/LangGraph

LLM workflows

Kubernetes

Helm deployments

CI/CD observability

Education

Bachelor of Computer Science

Tools

FastAPI

Async patterns

LangChain

LangGraph

Azure/AKS

Job description

Job Description

• Build and productionize cloud native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
• Build event driven, resilient integrations and containerized services, with hands‑on Kubernetes debugging and Helm-based deployments.
• Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
• Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.

Must Have Technical/Functional Skills

• 13+ years of experience with IT
• Build and productionize cloud native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
• Build event driven, resilient integrations and containerized services, with hands‑on Kubernetes debugging and Helm-based deployments.
• Establish observability, SLOs, CI/CD automation, testing
• Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
• Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.

Roles & Responsibilities

• Build and productionize cloud native backend services and AI/LLM inference pipelines.
• Design and develop Python-based APIs and microservices (FastAPI, async patterns) and agentic AI workflows using LangChain/LangGraph.
• Implement and optimize LLM capabilities including embeddings, RAG, vector search, prompt/context engineering, and model versioning.
• Package, serve, and monitor models for real-time and batch inference, ensuring operational readiness and performance.
• Build event driven, resilient integrations and containerized services, with hands‑on Kubernetes debugging and Helm-based deployments.
• Establish observability, SLOs, CI/CD automation, testing
• Apply strong systems design principles (concurrency, caching, reliability, rate limiting) and robust data engineering practices.
• Cloud exposure preferred (Azure/AKS, managed services), with bonus experience in performance tuning, frontend collaboration, and model governance/monitoring.

Salary Range

Salary Range: $110,000 to $130,000 per year

Qualifications

BACHELOR OF COMPUTER SCIENCE