20 days ago
Base Salary
$130k - $155k/yr
Responsibilities
- Lead the architecture, design, development, deployment, and optimization of machine learning models and agentic AI systems.
- Build and maintain cloud-native AI platforms, data ingestion pipelines, memory frameworks, model-serving architectures, and enterprise integrations.
- Implement MLOps and AgentOps practices covering deployment, testing, monitoring, observability, governance, and performance optimization.
- Collaborate with data science, data engineering, product, DevSecOps, engineering, and business stakeholders to deliver production-ready AI solutions.
- Mentor engineers, remove technical impediments, evaluate emerging technologies, and promote engineering best practices.
Requirements
- 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; C++ experience is preferred.
- Hands-on experience with TensorFlow, PyTorch, and scikit-learn.
- Understanding of software engineering fundamentals, system design, object-oriented programming, version control, testing, and distributed systems.
- Experience deploying AI/ML solutions in cloud environments and implementing model versioning and operational monitoring.
- Knowledge of data engineering concepts, ETL processes, Spark/PySpark, distributed data processing, and large-scale data ecosystems.
- Preferred experience includes agentic AI applications, autonomous AI workflows, agent observability, MLflow, Amazon SageMaker Pipelines, GitHub Actions, Jenkins, CloudBees, model governance, explainability, drift detection, bias monitoring, AI risk management, databases, semantic data models, and knowledge graphs.
- Strong communication, stakeholder management, problem-solving, collaboration, statistics, probability, linear algebra, multivariate calculus, and predictive analytics skills.
- Experience coaching team members and mentoring engineering talent in Agile delivery environments.
Benefits
- Hybrid work requiring three days per week in a client or Cognizant office in New York, New York.
- Medical, dental, and vision insurance.
- Health Savings Account and Flexible Spending Accounts 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 and wellbeing and mental health resources.
- Professional development, training, certification opportunities, career growth, internal mobility, and associate recognition programs.
- Eligibility for a discretionary annual incentive program based on performance.
Tech Stack
Categories
About Cognizant
Cognizant (Nasdaq: CTSH) is an AI Builder and technology services provider, bridging the gap between AI investment and enterprise value. We build full-stack AI solutions powered by deep industry, process and engineering expertise — embedding an organization's unique context into technology systems that amplify human potential and drive tangible outcomes. From strategy to deployment, we help global enterprises move from AI ambition to AI impact and stay ahead in a fast-changing world. See how at cognizant.ai | Follow us @cognizant