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LLM Engineer (Remote)

Cognizant · Anywhere

Full-timePython

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About the Role

LLM Engineer About the role As LLM Engineer, you will make an impact by designing, building, optimizing, and deploying Large Language Model (LLM) and Small Language Model (SLM) solutions that power the enterprise Agent Factory. You will be a valued member of the AI Engineering team and work collaboratively with data scientists, platform engineers, product teams, and business stakeholders to deliver scalable, production-ready generative AI capabilities. In this role, you will: Design, build, fine-tune, deploy, and optimize LLM and SLM solutions for enterprise-scale use cases.Develop and maintain supervised fine-tuning, model adaptation, and evaluation pipelines.Apply advanced model optimization techniques including LoRA, QLoRA, distillation, quantization, and model compression to improve performance and efficiency.Assess commercial, open-source, and internally hosted foundation models based on quality, cost, scalability, and operational requirements.Collaborate with cross-functional teams to support domain-specific AI solutions using approved enterprise datasets and best practices for responsible AI.Work model: We strive to provide flexibility wherever possible. Based on this role's business requirements, this is a remote position open to qualified applicants in Louisville, KY. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs. *Please note that this position is not eligible for visa transfer or sponsorship now or at any time in the future* What you need to have to be considered 7+ years of experience in Software Engineering, AI Engineering, Machine Learning Engineering, Platform Engineering, or a related technical discipline.2+ years of hands-on experience developing and deploying Generative AI, LLM, SLM, or Agentic AI solutions in production environments.Experience building enterprise-grade AI applications utilizing LLMs, Retrieval-Augmented Generation (RAG), prompt engineering, APIs, and cloud or on-premises platforms.Strong understanding of model fine-tuning, adaptation, evaluation frameworks, and AI quality measurement methodologies.Proficiency in machine learning development, model deployment, and MLOps best practices.Experience working with Python and modern AI/ML frameworks and tools.These will help you stand out Knowledge of AI infrastructure, GPU optimization, and large-scale model serving.Experience implementing model governance, responsible AI, and evaluation frameworks.Familiarity with vector databases, embedding models, and advanced RAG architectures.Experience building enterprise AI platforms, multi-agent systems, or agent orchestration frameworks.Salary and Other Compensation: Applications will be accepted until September 17th, 2026. The annual salary for this position is between $ 81,337 to $141,500 depending on experience and other qualifications of the successful candidate. This position is also eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans. Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements: Medical/Dental/Vision/Life InsurancePaid holidays plus Paid Time Off401(k) plan and contributionsLong-term/Short-term DisabilityPaid Parental LeaveEmployee Stock Purchase PlanDisclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government issued ID during each interview.
Founded 1994

Cognizant Technology Solutions Corporation is an American multinational information technology consulting and outsourcing company, headquartered in Teaneck, New Jersey. It was originally founded in Chennai, India, as an in-house technology unit of Dun & Bradstreet in 1994. After a series of corporate restructurings, the company went public in 1998.

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