Role: Java Backend Developer
Location: This is an onsite role in Sunnyvale, CA. No Remote will be considered
Experience: 10+ years
Salary and Other Compensation:
The annual salary for this position is between $90,000/Year to $101,000/Year depending on the experience and other qualifications of the candidate.
This position is also eligible for Cognizant's discretionary annual incentive program and stock awards, based on performance and subject to the terms of Cognizant's applicable plans.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
Medical/Dental/Vision/Life Insurance
• Paid holidays plus Paid Time Off
• 401(k) plan and contributions
• Long-term/Short-term Disability
• Paid Parental Leave
• Employee Stock Purchase Plan
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Responsibilities- Design and implement resilient cloud native applications on Azure using advanced Java and Spring Boot to deliver scalable and maintainable enterprise solutions that support critical business processes
- Develop performant backend services and APIs using Spring Boot and advanced Java that enable secure data exchange across internal platforms and external partner systems
- Build and optimize Python based services and utilities to support AI and ML workflows including data preparation feature engineering and model integration into production systems
- Apply prompt engineering techniques to design test and refine prompts that improve accuracy reliability and relevance of AI driven features embedded in user facing and internal applications
- Design and integrate AI and ML concepts such as model selection feature pipelines and inference services to deliver intelligent capabilities that enhance decision making and automation
- Implement agentic AI patterns by creating modular autonomous agents that can coordinate tasks reason over complex workflows and interact with APIs to achieve defined business objectives
- Collaborate with architects product managers and other engineers to translate business requirements into technical designs that align with enterprise standards and long term technology strategy
- Ensure reliability security and observability of solutions by implementing robust logging monitoring authentication and authorization mechanisms aligned with organizational compliance practices
- Conduct code reviews provide constructive feedback and maintain high coding standards to ensure readability performance testability and long term maintainability of services and components
- Optimize application performance and cost efficiency on Azure by tuning resource utilization using managed services and applying best practices for caching scaling and storage
- Coordinate with data science and AI specialist teams to integrate models into production services ensuring robust interfaces efficient data flows and clear operational ownership
- Prepare technical documentation including solution designs deployment guides and operational runbooks that support smooth handover stable operations and future enhancements
- Support continuous improvement by analyzing production issues identifying root causes and implementing preventative enhancements that elevate reliability and user satisfaction
- Participate actively in agile ceremonies and hybrid collaboration sessions to plan sprints clarify requirements and provide transparent progress updates enabling predictable delivery
Qualifications- Possess strong experience in designing and developing applications using advanced Java and Spring Boot including hands on exposure to microservices patterns RESTful services and enterprise integration
- Demonstrate practical expertise in Azure cloud services such as compute storage networking and platform services with proven ability to design secure scalable and cost conscious architectures
- Show hands on proficiency in Python for scripting backend services data processing and integration tasks supporting AI ML workflows and operational automation activities
- Apply a solid understanding of AI and ML concepts including model lifecycle basics feature engineering evaluation metrics and deployment considerations in production environments
- Exhibit working experience with agentic AI approaches such as orchestrating multiple agents managing tool use and designing workflows that allow AI components to act autonomously within defined guardrails
- Display competency in prompt engineering techniques including prompt design prompt tuning and evaluation strategies that enhance generative AI solution quality and consistency
- Bring familiarity with modern engineering practices such as test automation CI CD version control branching strategies and code quality tools to support reliable and repeatable delivery
- Communicate clearly in hybrid work settings using collaboration tools and documentation practices to coordinate effectively with distributed teams and stakeholders
