Overview

Department: Development

Responsibilities

• Design, build, deploy and maintain scalable cloud-based distributed, multi-tiered, concurrent, cross platforms web applications core infrastructure systems that perform and scale in a multi-tenant cloud environment
• Adding innovative new features in our enterprise class, cloud-scale business intelligence and data analytics products
• Collaborate on UI design with interaction Web designers and backend developers
• Implement RESTful APIs for all new systems and product features
• Drive quality by writing unit, functional, load and performance tests
• Employ solid software engineering practices to ensure high-quality, unit tested, maintainable/extensible code and predictable schedules working in agile environment
• Tracing performance and infrastructure bottlenecks and implementing innovative solutions to resolve them
• Participate in design and code reviews
• Apply AI-assisted development practices to improve engineering productivity while ensuring generated outputs are reviewed, tested, secure, and maintainable.
• Contribute to the design, integration, evaluation, and operational monitoring of AI-enabled product or engineering workflows where applicable.
• Willingness to work across time zones to interact with different stakeholders located across US west coast, Europe and India

Qualifications

• BE/ME/MCA Computer Science degree
• 4 to 6 years of hard-core software development experience with solid foundation in data structures, algorithms and software design with strong analytical and debugging skills
• Mastery of Java and OO programming
• Expertise in building scalable, concurrent, distributed systems
• Demonstrated expertise with Java 8+, Java Frameworks (Netflix Karyon, DropWizard, Spring Boot), Object-Oriented Design and development principles; and Java Application Servers such as Apache Tomcat; and databases such as SQL Server, MongoDB
• Knowledge of design patterns (GoF, J2EE)
• Demonstrated expertise in software engineering practices (e.g., reading/generating software specs, design patterns, continuous integration)
• Working knowledge of designing, developing and maintaining RESTful based APIs
• Hands on experience with test-driven, agile development practices
• Experience with data architecture and ETL processes and tools
• Experience with Caching, Clustering, Distributed Computing
• Experience building infrastructure/platform for SaaS products is a huge plus
• Hands on experience configuring, using and optimizing systems such as search, caching, messaging, logging or monitoring
• Experience working with open source tools/frameworks such as ActiveMQ, elasticsearch, Jersey, DropWizard, Guice, nginx, tomcat etc. is a big plus
• Good understanding of SQL and relational databases
• Excellent communication and teamwork skills
• A high degree of self-motivation and flexibility with the ability to organize and prioritize multiple tasks
• Practical understanding of Generative AI, Large Language Models, prompt engineering, model limitations, and responsible AI usage.
• Hands-on experience using AI-assisted development tools for code generation, debugging, refactoring, unit-test creation, technical documentation, and codebase or architecture understanding.
• Ability to review, test, and validate AI-generated outputs for correctness, security, performance, scalability, and maintainability.
• Ability to evaluate and select suitable AI models based on the use case, output quality, latency, cost, context requirements, privacy, security, and operational constraints.
• Experience creating supervised AI-assisted workflows or agents using prompts, APIs, application context, enterprise data, and tools, with appropriate human approval controls.
• Working knowledge of integrating AI capabilities through APIs or SDKs, including structured outputs, authentication, error handling, retries, timeouts, rate limits, and fallback behavior.
• Understanding of evaluating and monitoring AI-enabled applications, including representative test cases, regression evaluation, logging, tracing, response quality, latency, usage, cost, failures, and human review.
• Understanding of responsible and secure AI practices, including protection of proprietary information, hallucination management, prompt-injection risks, output validation, least-privilege access, auditability, and human oversight.
• Familiarity with Retrieval-Augmented Generation, embeddings, vector search, or other grounding techniques is an added advantage.
• Demonstrated curiosity and willingness to continuously learn, assess, and apply emerging AI technologies to software-engineering problems.
• Right to Work
• Employee Polygraph Protection Act
• Family & Medical Leave Act