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AI​/ML Engineer

Remote / Online - Candidates ideally in
Aurora, Kane County, Illinois, 60290, USA
Listing for: Cognizant
Remote/Work from Home position
Listed on 2026-10-20
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Salary/Wage Range or Industry Benchmark: 88000 - 135000 USD Yearly USD 88000.00 135000.00 YEAR
Job Description & How to Apply Below

Job Title – Senior AI/ML Engineer


* Please note this role is not able to offer visa transfer or sponsorship now or in the future*

About the Role

We are seeking a highly skilled Senior AI/ML Engineer to design, develop, and deliver innovative AI-powered solutions that solve complex business challenges at enterprise scale. In this role, you will leverage your expertise in Machine Learning, Natural Language Processing (NLP), Generative AI, and Agentic AI to build production-ready systems that drive business value, accelerate decision-making, and enable intelligent automation.

You will work closely with cross-functional stakeholders, architects, data engineers, and business teams to translate business requirements into scalable, reliable, and governed AI solutions.

In This Role, You Will
  • Design, develop, and deploy production-grade AI and Machine Learning solutions.
  • Build and maintain advanced Generative AI and Agentic AI applications utilizing multi-agent orchestration frameworks.
  • Develop Retrieval-Augmented Generation (RAG) solutions and Natural Language-to-SQL (Text-to-SQL) applications.
  • Create and optimize LLM evaluation, monitoring, observability, and governance frameworks.
  • Design scalable NLP solutions, including text classification, semantic search, topic modeling, sentiment analysis, and embedding-based applications.
  • Develop and deploy machine learning models using statistical and deep learning techniques.
  • Build high-performance data pipelines and AI workflows using Python, PySpark, SQL, and cloud-native technologies.
  • Implement MLOps best practices including CI/CD, model validation, monitoring, drift detection, and automated quality controls.
  • Collaborate with stakeholders to understand business requirements and deliver scalable AI solutions aligned with organizational objectives.
  • Ensure AI systems adhere to enterprise governance, security, compliance, and responsible AI standards.
  • Contribute to AI platform architecture, solution modernization initiatives, and continuous improvement efforts.

We strive to provide flexibility wherever possible. Based on this role’s business requirements, this is a remote position open to qualified applicants in the United States. Regardless of your working arrangement, we are here to support a healthy work-life balance though our various wellbeing programs.

The working arrangements for this role are accurate as of the date of posting. This may change based on the project you’re engaged in, as well as business and client requirements. Rest assured; we will always be clear about role expectations.

What You Need to Have to Be Considered
  • Bachelor’s degree in computer science, Data Science, Engineering, Mathematics, or a related field.
  • 8+ years of experience in Machine Learning, Artificial Intelligence, Data Science, or related disciplines.
  • Strong hands-on experience developing and deploying enterprise AI/ML solutions in production environments.
  • Proven experience with Generative AI technologies, including RAG, agent-based systems, prompt engineering, and LLM evaluation.
  • Expertise in Python and AI/ML libraries such as PyTorch, Tensor Flow, Scikit-Learn, and Hugging Face.
  • Experience with Lang Graph and/or Lang Chain for AI application development.
  • Strong knowledge of NLP techniques including classification, semantic similarity, embeddings, clustering, and topic modeling.
  • Experience designing and implementing scalable data pipelines using SQL and PySpark.
  • Experience deploying solutions on cloud platforms such as AWS, Azure, or Google Cloud Platform.
  • Knowledge of containerization and orchestration technologies including Docker and Kubernetes.
  • Experience implementing MLOps practices, including model deployment, monitoring, validation, CI/CD, and observability.
  • Strong communication…
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