- 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 contemporary 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.