▷ [Urgent Search] Data Integration & LLM Engineer
4 weeks ago
About the Role
We are seeking a highly motivated Software Engineer with a strong foundation in Java (Spring Boot), data integration, and a growing expertise in Large Language Models (LLMs). This role is ideal for engineers who enjoy working at the intersection of scalable data systems and AI-driven applications, building robust pipelines while also exploring cutting-edge generative AI solutions.
Key Responsibilities
- Design and implement data integrations including APIs, SaaS connectors, and ETL/ELT pipelines to ensure reliable and scalable data flows.
- Build and maintain backend services and applications using Java (Spring Boot or equivalent frameworks).
- Develop Python-based workflows for AI/ML pipelines, experimentation, and automation scripting.
- Integrate and experiment with LLMs (OpenAI, Anthropic, LLaMA, Mistral, etc.) for use cases such as retrieval-augmented generation (RAG), summarization, and intelligent data insights.
- Implement vector search solutions using Pinecone, Weaviate, Milvus, or FAISS for LLM-backed applications.
- Collaborate with product, data, and ML teams to design end-to-end solutions that combine data engineering with AI capabilities.
- Ensure systems meet high standards of performance, scalability, security, and compliance.
Required Qualifications
- Strong programming experience in Java (Spring Boot or equivalent frameworks).
- Familiarity with Python, particularly for AI/ML workflows and scripting.
- Proven experience with data integrations: APIs, SaaS connectors, ETL/ELT pipelines.
- Exposure to LLMs (OpenAI, Anthropic, LLaMA, Mistral, etc.) and associated frameworks (LangChain, LlamaIndex, Hugging Face Transformers).
- Experience working with databases (SQL/NoSQL) and vector search technologies (Pinecone, Weaviate, Milvus, FAISS).
Preferred Skills
- Knowledge of cloud platforms (AWS, GCP, or Azure) for deploying scalable systems and ML workloads.
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Understanding of data governance, observability, and security best practices.
- Interest in generative AI advancements and a passion for building practical applications on top of them.
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