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# SR Gen AI developer-

- **Company:** Cognizant Technology Solutions Corporation
- **Location:** Topeka, KS, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Microsoft Azure, Open Source Technology, Azure Machine Learning, Data Logging, GitHub Copilot, Office365, Large Language Models, ONNX (Open Neural Network Exchange) Format, TensorRT
- **Published:** August 8, 2026
- **Apply:** https://www.kansasworks.com/jobs/13634771

## About the Role

All contractor resources are expected to demonstrate baseline proficiency in enterprise-approved AI tools as part of their day-to-day responsibilities. This includes, but is not limited to:

 + Consistent Use: Maintain a minimum of 90% weekly usage of AI tools such as GitHub Copilot, Microsoft 365 Copilot, and other GenAI platforms approved by the enterp

## Description

This role will work closely with product, engineering, and data teams to build scalable AI features that are reliable, performant, and secure in production environments.

Responsibilities

 + Design and implement end-to-end AI features, including RAG systems, agent workflows, embeddings pipelines, and fine-tuned LLM applications.

 + Build and maintain data ingestion, chunking, embedding, retrieval, and prompt orchestration pipelines.

 + Fine-tune and evaluate open-source and proprietary LLMs using techniques such as LoRA, QLoRA, PEFT, and supervised fine-tuning.

 + Deploy and serve custom LLMs in Azure environments using Azure Machine Learning, AKS, Container Apps, or related services.

 + Optimize inference performance using quantization, batching, model parallelism, and GPU-aware serving strategies.

 + Work with model formats such as Safetensors, GGUF, ONNX, and TensorRT for efficient loading, portability, and deployment.

 + Build scalable APIs and services for LLM inference, streaming responses, and model orchestration.

 + Implement model monitoring, logging, evaluation, prompt testing, and feedback loops.

 + Collaborate with product managers and stakeholders to translate business requirements into AI capabilities.

 + Explain technical trade-offs clearly to non-technical audiences, including model size, quantization, latency, cost, and accuracy.


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