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Data Integration & LLM Engineer

Data Integration & LLM Engineer

Chargebeethane, maharashtra, in
6 days ago
Job description

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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