Manager-Data & ML Engg

Cognizant; Hyderabad; Hybrid; INR 4,000,000 - 6,000,000; Full time

Job summary

Cognizant seeks a Manager of Data and ML Engineering to design, build, and optimize scalable data platforms and ML solutions using Java, advanced Java, and JavaScript in a hybrid work setting. You will work with global stakeholders to deliver secure, high-quality data products for analytics and intelligent applications.

The role focuses on building robust data pipelines, modular services and production ML workflows, with strong emphasis on data quality, observability, and collaboration across

Qualifications

• Demonstrated hands-on experience designing data-intensive applications using Java with a strong focus on scalability and reliability.
• Proficient with modern JavaScript frameworks to build or integrate front-end or service layer components that access data and ML platforms.
• Understanding of machine learning lifecycle including feature pipelines, model training, evaluation and deployment in production environments.
• Experience working in hybrid work models, collaborating across onsite and remote teams with structured communication.
• Knowledge of relational and non-relational databases, data warehousing concepts and streaming platforms for batch and real-time data.
• Strong software engineering fundamentals: algorithms, data structures, design patterns, testing strategies and secure coding practices.
• Ability to communicate complex technical concepts to both technical and non-technical stakeholders and align solutions with business goals.

Responsibilities

• Architect robust data pipelines and platforms handling large-scale datasets with Java to support analytics and ML initiatives.
• Design modular services and APIs using Java and JavaScript to expose data and ML capabilities to internal applications and partner systems.
• Develop production-grade ML workflows covering data prep, feature engineering, model training and deployment with traceability.
• Implement data quality controls including validation, anomaly detection, reconciliation and monitoring.
• Optimize performance of batch and real-time data processing through code tuning, parallelization, and efficient resource use.
• Collaborate with data scientists, analysts and product teams to translate requirements into scalable engineering solutions.
• Establish coding standards, reviews, and version control workflows to improve reliability and security of data/ML codebases.
• Coordinate with cloud infra and security teams to ensure compliance with enterprise standards and regulatory expectations.
• Create detailed technical documentation for data models, transformation logic, interfaces and ops procedures.
• Enable observability with logging, metrics and alerting for data/ML pipelines to detect and resolve issues quickly.
• Mentor junior engineers and share best practices to raise team capability and delivery quality.
• Drive continuous improvement by evaluating new tools, frameworks, and patterns in Java/JS ecosystems.
• Align engineering activities with sustainability goals by prioritizing efficient architectures that reduce resource use and support ethical data use.

Skills

Java

Advanced Java

Advanced JavaScript

Data pipelines

Data modeling

ML lifecycle

Code quality

Security best practices

Job description

Job Summary

Manager Data and ML Engineering role focusing on designing building and optimizing scalable data platforms and machine learning solutions using Java advanced JavaScript and advanced Java in a hybrid work model enabling secure high quality data products that power analytics and intelligent applications for global business stakeholders in a collaborative and inclusive environment

Responsibilities

• Architect robust data pipelines and platforms that ingest transform and serve large scale datasets using Java and advanced Java to support analytics and machine learning initiatives for diverse business functions
• Design modular and reusable services and APIs using Java and advanced JavaScript that reliably expose data and ML capabilities to internal applications and external partner systems
• Develop production ready machine learning workflows that manage data preparation feature engineering model training and model deployment while ensuring traceability and reproducibility across environments
• Implement strong data quality controls including validation checks anomaly detection data reconciliation and monitoring to maintain trusted datasets for decision making
• Optimize performance of batch and real time data processing jobs by tuning code parallelization strategies and resource usage to achieve reliable throughput and cost efficiency
• Collaborate closely with data scientists analysts and product teams to translate analytical requirements into scalable engineering solutions that deliver measurable business outcomes
• Establish coding standards review practices and version control workflows that improve reliability maintainability and security of data and ML engineering codebases
• Coordinate with cloud infrastructure and security teams to ensure data pipelines storage layers and ML services adhere to enterprise standards and regulatory expectations
• Create detailed technical documentation for data models transformation logic interface contracts and operational procedures that help teams onboard and maintain solutions efficiently
• Enable robust observability by implementing logging metrics and alerting for data and ML pipelines so that issues are detected early and resolved with minimal business disruption
• Mentor junior engineers by reviewing work sharing best practices and guiding them through complex engineering problems to raise overall team capability and delivery quality
• Drive continuous improvement by evaluating new tools frameworks and design patterns in the Java and JavaScript ecosystem and proposing adoption where they add clear value
• Align engineering activities with organizational sustainability and social impact goals by prioritizing efficient architectures that reduce resource consumption and support ethical use of data

Qualifications

• Demonstrate ten to fourteen years of hands on experience in designing and implementing data intensive applications using Java and advanced Java with strong focus on scalability and reliability
• Exhibit advanced proficiency with modern JavaScript frameworks and tooling to build or integrate front end or service layer components that interact with data and ML platforms
• Apply solid understanding of machine learning lifecycle including feature pipelines model training evaluation and deployment using industry standard tools and libraries
• Show proven experience working in hybrid work models by effectively collaborating across onsite and remote teams using structured communication and documentation practices
• Display strong knowledge of relational and nonrelational databases data warehousing concepts and streaming platforms to support both batch and real time data solutions
• Utilize deep expertise in software engineering fundamentals including algorithms data structures design patterns testing strategies and secure coding practices
• Communicate complex technical concepts clearly to technical and nontechnical stakeholders while gathering feedback and aligning solutions to business objectives