AI/LLM Engineer
2 days ago
Key Responsibilities :
Design and implement end-to-end data pipelines for training and fine-tuning LLMs, including dataset creation, cleaning, augmentation, and labeling workflows.
Apply advanced RAG techniques, prompt engineering, and model fine-tuning (LoRA, PEFT, adapters) for domain-specific use cases.
Integrate AI models with backend and frontend systems via APIs, batching, caching, and streaming responses.
Deploy and optimize LLMs & embeddings using APIs and/or open-source models (OpenAI, Anthropic, LLaMA-family, Mistral, etc.).
Develop and maintain secure AI APIs using FastAPI/gRPC with Kubernetes and CI/CD pipelines.
Implement safety and compliance layers including prompt injection defenses, hallucination reduction, and PII redaction.
Collaborate with MLOps and platform engineering teams to ensure scalable deployment using Docker, Kubernetes, and Ray/Serve.
Leverage frameworks such as LangChain, Hugging Face Transformers, and Azure OpenAI for model orchestration and integration.
Work with vector databases (FAISS, Pinecone, Milvus) to build efficient retrieval-augmented generation pipelines.
Mandatory Skills:
Strong experience with LLMs, embeddings, and fine-tuning techniques.
Proficiency in Python and experience in LangChain/Hugging Face.
Hands-on experience with MLOps (Docker, Kubernetes, CI/CD).
Strong knowledge of model integration and scalable API development.
Familiarity with safety and compliance mechanisms in AI systems.
Desirable / Plus:
- Exposure to speech-to-text models (e.g., Whisper) with emphasis on Indian languages.
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