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Lead Machine Learning Engineer (Agentic AI)About the RoleAs a
Lead Machine Learning Engineer, you will make an impact by leading the engineering, deployment, and optimization of Agentic AI systems and machine learning solutions that solve complex business challenges. You will be a valued member of the AI and Data Engineering team and work collaboratively with Data Scientists, Data Engineers, Data Analysts, DevSecOps professionals, Product teams, and business stakeholders to deliver scalable, production-ready AI capabilities that create measurable business value.
This role combines technical leadership, engineering excellence, and innovation. You will help build next-generation AI products and services while fostering a culture of continuous learning, collaboration, and agile delivery.
In This Role, You Will- Lead the architecture, design, development, deployment, and optimization of machine learning models and Agentic AI systems that solve complex business problems.
- Partner with Data Science, Product, Engineering, and DevSecOps teams to deliver scalable, secure, and production-ready AI solutions.
- Build and maintain cloud-native AI platforms, data ingestion pipelines, memory frameworks, model-serving architectures, and enterprise integrations.
- Implement MLOps and AgentOps best practices, including CI/CD/CT pipelines, automated testing, model monitoring, observability, governance, and performance optimization.
- Mentor engineers, remove technical impediments, evaluate emerging technologies, and promote engineering best practices across AI initiatives.
Work ModelWe believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a
hybrid position requiring 3 days per week in a client or Cognizant office in
New York, New York. Regardless of your working arrangement, we are here to support a healthy work-life balance through 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, Engineering, Information Technology, or a related field, or equivalent professional experience.
- Extensive experience designing, developing, deploying, and supporting machine learning and AI solutions in enterprise environments.
- Strong programming expertise in Python and SQL, with experience in C++ preferred.
- Hands-on experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
- Strong understanding of software engineering fundamentals, including system design, object-oriented programming, RESTful APIs, microservices, version control, testing, and distributed systems.
- Experience developing and deploying AI/ML solutions within cloud environments.
- Experience implementing CI/CD pipelines, automated deployment processes, model versioning, and operational monitoring.
- Strong knowledge of data engineering concepts, including ETL processes, Spark/PySpark, distributed data processing, and large-scale data ecosystems.
- Excellent communication, stakeholder management, problem-solving, and collaboration skills.
- Experience working within Agile delivery environments and coaching team members.
These Will Help You Stand Out- Experience building and deploying Agentic AI applications and autonomous AI workflows.
- Knowledge of Agent Development Life Cycle (ADLC) methodologies and agent observability platforms.
- Experience utilizing LLM development tools and AI-assisted engineering platforms.
- Hands-on experience with MLFlow, Amazon SageMaker Pipelines, GitHub Actions, Jenkins, CloudBees, or similar MLOps technologies.
- Knowledge of model governance, explainability, drift detection, bias monitoring, and AI risk management practices.
- Experience working with relational, NoSQL, graph databases, semantic data models, and knowledge graph technologies.
- Strong understanding of statistics, probability, linear algebra, multivariate calculus, and predictive analytics.
- Demonstrated ability to mentor and develop engineering talent while fostering innovation and continuous improvement.
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.
Salary and Other CompensationThe annual salary for this position is anticipated to be between
$130,000 and $155,000, depending on experience, qualifications, geographic location, skills, and other job-related factors.
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.
BenefitsCognizant offers a comprehensive and competitive benefits package designed to support the health, wellbeing, and financial security of our associates and their families, including:
- 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
Benefits may vary by location and employment status and are subject to change.
Application DeadlineApplications will be accepted until
September 30, 2026.
Cognizant reserves the right to close this posting earlier based on application volume, business needs, or hiring timelines.
