Key Responsibilities:

- Model Deployment & MLOps: Integrate machine learning models into Java-based microservices and scale them using containerization tools like Docker and orchestration platforms like Kubernetes.

- AI/LLM Integration: Leverage frameworks like Spring AI to embed, fine-tune, or connect to language models (e.g., OpenAI, Anthropic, AWS Bedrock).

- Data Pipelines & RAG: Build data pipelines for Retrieval-Augmented Generation (RAG), working with structured/unstructured data, vector databases, and embedding techniques.

- Algorithm & API Development: Develop and deploy algorithms for classification, regression, and NLP while wrapping them in RESTful APIs or Spring WebFlux.

- Model Optimization & Monitoring: Implement observability, guardrails, and automated evaluation frameworks to ensure production models are performing optimally and securely.

Essential Qualifications & Skills:

- Core Languages: Strong hands-on coding proficiency in Java (especially Spring Boot) along with working knowledge of Python for AI prototyping and glue code.

- AI & ML Frameworks: Familiarity with modern ML libraries like TensorFlow or PyTorch, and vector store integrations.

- Software Engineering: Experience building robust, scalable microservices, RESTful APIs, and CI/CD pipelines.

- Data Engineering: Understanding of exploratory data analysis, feature engineering, and data scaling.

- Education: Bachelor's or masters degree in computer science, Machine Learning, or a closely related quantitative field.