Software Engineer, Senior ( Java , AWS , AI - Ahmedabad )
Summary
Senior Java engineer at Infor's Birst office in Ahmedabad, building and sustaining a cloud-scale, multi-tenant business intelligence and analytics platform — designing distributed backend services, REST APIs, and tests, with growing use of AI-assisted development. Stack: Java, Spring Boot/DropWizard, AWS, SQL Server, MongoDB.
- 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
- 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.
Skills
- ActiveMQ
- Agile
- AI
- Analytics
- API
- Authentication
- Business Intelligence
- CI/CD
- Cloud
- Data Analytics
- Design Patterns
- Distributed Computing
- Distributed Systems
- Dropwizard
- Elasticsearch
- Embeddings
- ETL
- Generative AI
- J2EE
- Java
- LLM
- MongoDB
- Nginx
- OOP
- Prompt Engineering
- RAG
- RDBMS
- REST
- SaaS
- Spring
- SQL
- SQL Server
- Tomcat
- Unit Testing
- Vector Search