Key skills
Machine Learning EngineeringGenerative AIMLOpsAWSPythonTensorFlowPyTorchHugging FaceLangChainAmazon SageMakerCI/CD pipelinesInfrastructure as CodeDockerKubernetesGitMachine Learning Lifecycle ManagementModel GovernanceAWS S3AWS LambdaAWS Step FunctionsAWS API GatewayAWS CloudWatchAWS ECSAWS IAMLarge Language ModelsPrompt EngineeringEmbeddingsVector DatabasesRetrieval-Augmented Generation (RAG)AIMachine LearningMLOpenAIClaudeAnthropicLlamaRAGData EngineeringECSEKSLambdaS3IAMCloudWatchSageMakerBedrockAPI GatewayCI/CDRisk ManagementCommunicationCollaboration
About this role
Cognizant is seeking a Senior Machine Learning Engineer to design, build, and deploy scalable AI and machine learning solutions. The role involves collaborating with various teams to create innovative AI solutions using AWS and modern MLOps practices.
Responsibilities:
- 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
Requirements:
- 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
- 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