Key Responsibilities
Design and develop high-performance applications using Java (Spring Boot, Microservices).
Integrate AI models via REST APIs, Python services, or cloud AI platforms.
Collaborate with data scientists to deploy and optimize ML models in production.
Build APIs and microservices that enable intelligent, data-driven features.
Implement data pipelines for AI workloads, ensuring scalability and reliability.
Evaluate and experiment with GenAI, LLMs, and AI APIs (OpenAI, AWS Bedrock, Vertex AI, OpenAI).
Maintain coding standards, CI/CD pipelines, and cloud deployment best practices (AWS, GCP).
Troubleshoot performance issues and ensure application reliability.
Required Qualifications:
Bachelor's degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
At least 4 years of experience in Information Technology.
Experience in Java/J2EE development
Location for this position is
Austin, TX.
This position requires travel and/or relocation to project/client location.
Preferred Qualifications:
At least 3 years of experience in Java/J2EE development
At least 3 years of experience in DB SQL/NoSQL.
Strong knowledge of Spring Boot, Microservices, Spring Security, Spring MVC, Spring Data, JPA, Hibernate.
Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch, scikit-learn).
Experience integrating AI APIs (OpenAI, Hugging Face, Google Vertex AI).
Hands-on experience designing and integrating microservices using REST APIs and asynchronous messaging (Kafka).
High-level knowledge of CI/CD.
Familiarity with Generative AI technologies (LLM integration, prompt engineering, AI model APIs).
Solid understanding of data structures, algorithms, and software design patterns.
Familiarity with Python for ML model interaction or API wrapping.
Experience with Docker, Kubernetes, and cloud environments (AWS/GCP/Azure).
Exposure to LangChain, LangGraph, RAG architecture, or vector databases (Pinecone, FAISS).
Understanding of the machine learning lifecycle (training, testing, deployment).
Experience with event-driven systems (Kafka, RabbitMQ).
Contribution to AI-based open-source projects or hackathons.
Strong analytical and troubleshooting skills.
Excellent oral and written communication skills.
Ability to independently learn new technologies.
Passionate, team player, and fast learner.