Software Engineer -AI

APIs followingdefined architectural standards; integrate with relational databases.

• Implement business logic,authentication mechanisms (JWT-based security), and microservices-basedapplications.

• Design, develop, and deploy AI/MLmodels (NLP, predictive analytics, computer vision).

• Implement Retrieval-AugmentedGeneration (RAG) architectures to enhance AI solutions with knowledgeretrieval.

• Prepare data pipelines fortraining, preprocessing, and feature engineering.

• Containerize applications usingDocker and support CI/CD pipelines and cloud deployments (AWS preferred).

• Write clean, testable, andmaintainable code; implement unit and integration tests (JUnit, Pytest).

• Collaborate with cross-functionalteams to integrate AI/ML solutions into production-ready applications.

• Monitor and optimize model andapplication performance; troubleshoot and resolve technical issues.

• Participate in code reviews, agileceremonies, and knowledge-sharing initiatives.

You have what it takes if you have...

**Technical Requirements
**

**Programming
**

• Proficiency in Java 17+ and Python3.x

• Java: Spring Boot (REST APIs,configuration), Spring Data JPA, Spring Security basics

• Python: FastAPI, Pydantic,SQLAlchemy, async/await concepts, Pytest

AL/ML

• Knowledge of machine learning frameworks (TensorFlow, PyTorch, scikit-learn)

• Experience in RAG architectures, model training, evaluation, optimization, and deployment

• Familiarity with large language models (LLMs) is a plus

**Architecture & APIs
**

• REST API development

• Basic understanding of microservices concepts

• Familiarity with OpenAPI/Swagger

**Database
**

• Experience with PostgreSQL/MySQL

• Writing queries and understanding relationships

• Exposure to migration tools (e.g., Alembic preferred)

**Cloud & DevOps
**

• Docker (basic containerization)

• Exposure to AWS services (EKS, RDS preferred)

• Familiarity with CI/CD pipelines (GitHub Actions or similar)

• Basic logging and monitoring understanding

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