"Please note, this role is not able to offer visa transfer or sponsorship now or in the future." About the role As a Senior Machine Learning Engineer you will make an impact by designing, building, and deploying scalable AI and machine learning solutions that drive business innovation and measurable outcomes. You will be a valued member of the AI & Data Engineering team and work collaboratively with architects, data engineers, product owners, business stakeholders, and cross-functional technology teams. In this role, you will: • Design, develop, and deploy machine learning and Generative AI solutions using AWS cloud-native services and modern MLOps practices. • Build and operationalize end-to-end MLOps pipelines for model training, validation, deployment, monitoring, and lifecycle management using Amazon SageMaker. • Develop Generative AI applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering techniques. • Implement scalable, secure, and resilient AI/ML platforms using Docker, Kubernetes/EKS, CI/CD pipelines, and Infrastructure as Code practices. • Partner with technical and business stakeholders to translate requirements into production-ready AI solutions while ensuring performance, reliability, governance, and cost optimization. Work model We strive to provide flexibility wherever possible. Based on this role's business requirements, this position is remote. The working arrangements for this role are accurate as of the date of posting. This may change based on the project you are engaged in, as well as business and client requirements. What you need to have to be considered • Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field. • 6-10 years of software development experience, including at least 3 years in Machine Learning Engineering, AI Engineering, or MLOps. • Strong proficiency in Python and hands-on experience with AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, or similar technologies. • Hands-on experience with Amazon SageMaker and AWS AI/ML services for model development, deployment, and monitoring. • Experience building and supporting MLOps pipelines using CI/CD automation, Infrastructure as Code, and cloud-native development practices. • Knowledge of Generative AI concepts, including LLMs, prompt engineering, embeddings, vector databases, and RAG architectures. • Experience with Docker, Kubernetes/EKS, Git, and modern software engineering best practices. • Strong understanding of machine learning lifecycle management, model governance, observability, and production support. • Experience working with AWS services such as S3, Lambda, Step Functions, API Gateway, CloudWatch, ECS/EKS, and IAM. • Strong analytical, problem-solving, collaboration, and communication skills. This will help you stand out • Experience designing and deploying enterprise-scale Generative AI solutions. • Hands-on experience with Amazon Bedrock and foundation models such as OpenAI, Anthropic Claude, Llama, or similar platforms. • Experience in highly regulated industries such as Healthcare, Insurance, or Financial Services. • Knowledge of Responsible AI, AI Governance, Model Risk Management, and compliance frameworks. • Experience with data engineering frameworks and large-scale data processing technologies. • AWS Certified Machine Learning - Specialty certification. • AWS Certified Solutions Architect (Associate or Professional) certification. • AWS Certified Developer - Associate certification. • Additional certifications in MLOps, AI Engineering, or Generative AI technologies. We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role. Applications will be accepted until 07/27/2026 The annual salary range for this position is between $110.000 - $130.000 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 Insurance • Paid holidays plus Paid Time Off • Long-term/Short-term Disability • Paid Parental Leave Disclaimer: 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.