Machine Learning Engineer

Cognizant is hiring a Machine Learning Engineer focused on Agentic AI to build production-ready machine learning solutions for complex business problems. This hybrid role in New York, NY combines model development with engineering practices that bring AI systems to life in real environments.

You will work across Data Science, Product, Engineering, and DevSecOps to design and optimize agentic capabilities, cloud-native AI pipelines, and model-serving architectures. The role also emphasizes MLOps and AgentOps discipline, including automation, monitoring, and governance from development through deployment.

What you’ll do

• Design, develop, deploy, and optimize machine learning models and Agentic AI systems that address real-world business challenges.
• Collaborate with Data Science, Product, Engineering, and DevSecOps teams to deliver scalable, secure, production-ready AI solutions.
• Build and maintain cloud-native AI applications, data ingestion pipelines, memory frameworks, and model-serving architectures.
• Apply MLOps and AgentOps best practices, including automated testing, CI/CD/CT pipelines, monitoring, observability, and model governance.
• Drive continuous improvement by evaluating emerging technologies and applying engineering best practices across AI development projects.

Required qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Technology, or related field, or equivalent professional experience.
• Experience designing, developing, deploying, and supporting machine learning applications in enterprise environments.
• Strong programming skills in Python and SQL, with exposure to C++ preferred.
• Experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Knowledge of software engineering principles including object-oriented programming, RESTful APIs, microservices, testing, version control, and system design.
• Experience developing and deploying ML solutions in cloud-based environments.
• Familiarity with CI/CD pipelines, automated deployment practices, model versioning, and monitoring frameworks.
• Knowledge of data engineering concepts including ETL, Spark/PySpark, distributed processing, and large-scale data environments.
• Strong communication, problem-solving, and collaboration skills.
• Experience working in Agile development environments.

Technologies

• Python, SQL, C++, TensorFlow, PyTorch, scikit-learn
• RESTful APIs, microservices
• CI/CD pipelines and CI/CD/CT pipelines
• MLOps, AgentOps
• ETL, Spark, PySpark
• MLFlow
• Amazon SageMaker Pipelines
• GitHub Actions, Jenkins, CloudBees
• LLM development tools, AI-assisted software engineering platforms
• Relational databases, NoSQL databases, graph databases

Additional strengths

• Experience developing Agentic AI applications and autonomous AI workflows.
• Familiarity with Agent Development Life Cycle (ADLC) methodologies and observability frameworks.
• Experience using LLM development tools and AI-assisted software engineering platforms.
• Knowledge of MLFlow, Amazon SageMaker Pipelines, GitHub Actions, Jenkins, CloudBees, or similar MLOps technologies.
• Understanding of model governance, explainability, drift detection, bias monitoring, and AI risk management practices.
• Experience working with relational, NoSQL, and graph databases.
• Knowledge of statistics, probability, linear algebra, predictive analytics, and machine learning optimization techniques.
• Commitment to continuous learning and keeping current with emerging AI technologies.

Work model

• Hybrid: 3 days per week in a client or Cognizant office in New York, New York.

Salary and incentives

• Annual salary anticipated between $110,000 and $135,000, depending on experience, qualifications, geographic location, skills, and other job-related factors.
• Eligible for Cognizant’s discretionary annual incentive program, subject to the terms of applicable plans.

Benefits

• Medical, dental, and vision insurance
• Health Savings Account (HSA) and Flexible Spending Accounts (FSA), where applicable
• Company-paid life insurance and disability coverage
• 401(k) retirement savings plan with company contributions, subject to plan provisions
• Paid time off, company holidays, and leave programs
• Employee Assistance Program (EAP)
• Wellbeing and mental health resources
• Professional development, training, and certification opportunities
• Career growth and internal mobility programs
• Associate recognition and reward programs

Application deadline: Applications will be accepted until September 30, 2026.