AI Engineer
Description
Experience: 1-2 Years
Employment Type: Full-Time
- Develop and maintain scalable backend services using Java and Spring Boot.
- Build REST APIs and backend components for AI-powered enterprise applications.
- Contribute to the development of Retrieval-Augmented Generation (RAG) applications.
- Work with vector databases/search technologies to support semantic search and retrieval.
- Develop data ingestion and processing workflows for AI applications.
- Implement structured JSON-based inputs and outputs, including schema validation and error handling.
- Integrate AI services with enterprise databases, APIs, and backend systems.
- Contribute to AI orchestration and agentic workflows under the guidance of senior engineers.
- Write clean, maintainable, testable, and well-documented code.
- Apply object-oriented programming and established software design patterns.
- Participate in code reviews, debugging, performance optimization, and production support.
- Work with cloud-based deployments and containerized applications on AWS.
- Strong Java and Spring Boot fundamentals are essential.
- Good problem-solving and debugging skills.
- Ability to understand existing systems and contribute to production-grade applications.
- Strong interest in Generative AI and emerging AI technologies.
- Willingness to learn new AI frameworks, tools, and cloud technologies.
- Good communication and collaboration skills.
- Ability to work effectively with senior engineers, architects, and AI/ML teams.
- 1-2 years of hands-on software development experience.
- Strong programming skills in Java.
- Good hands-on experience with Spring / Spring Boot.
- Strong understanding of:
- Object-Oriented Programming.
- Collections and Exception Handling.
- Java Streams and Lambda expressions.
- REST APIs.
- Multithreading and concurrency basics.
- Dependency Injection.
- Spring Boot application development.
- Understanding of common design patterns and clean coding practices.
- Experience with unit testing using frameworks such as JUnit and Mockito.
- Good knowledge of relational databases such as PostgreSQL or MySQL.
- Strong understanding of SQL, joins, indexing, and query optimization basics.
- Exposure to MongoDB or another NoSQL database.
- Basic to good understanding of Generative AI and LLM concepts.
- Understanding of RAG concepts, including:
- Document ingestion
- Chunking
- Embeddings
- Vector search
- Retrieval
- Prompt construction
- Understanding of prompt engineering and structured LLM outputs.
- Interest in learning and working with AI agents and agentic workflows.
- Basic hands-on experience with AWS services.
- Understanding of cloud deployment concepts and containerized applications.
- Exposure to Docker and CI/CD pipelines is preferred.
- Familiarity with services such as AWS ECS, EKS, Lambda, S3, or similar services is a plus.
Skills
- Agentic AI
- AI
- API
- AWS
- CI/CD
- Cloud
- Data Ingestion
- Design Patterns
- DevOps
- Docker
- ECS
- EKS
- Embeddings
- Generative AI
- Java
- JSON
- JUnit
- Lambda
- LLM
- Machine Learning
- Mockito
- MongoDB
- Multithreading
- MySQL
- NoSQL
- OOP
- PostgreSQL
- Prompt Engineering
- RAG
- RDBMS
- REST
- S3
- Semantic Search
- Spring
- SQL
- Unit Testing
- Vector Databases
- Vector Search