GenAI Engineer

3 weeks ago

Delhi, India Soul Ai Full time

Job Description Key Responsibilities: - Fine-tune and deploy LLMs (e.g., GPT, LLaMA, Mistral) using frameworks like Hugging Face Transformers and LangChain. - Build and optimize RAG pipelines with vector databases (e.g., Pinecone, FAISS, Weaviate). - Engineer prompts for structured and reliable outputs across diverse use cases such as chatbots, summarization tools, and coding assistants. - Implement scalable inference pipelines; optimize for latency, throughput, and cost using quantization, distillation, and other model optimization techniques. - Collaborate with product, design, and engineering teams to integrate generative AI capabilities into user-facing features. - Monitor and improve model performance, accuracy, safety, and compliance in production. - Ensure responsible AI practices through content filtering, output sanitization, and ethical deployment. Required Skills: - Proficiency in Python and familiarity with modern machine learning tools and libraries. - Hands-on experience with LLM development using Hugging Face Transformers, LangChain, or LlamaIndex. - Experience building and deploying RAG pipelines, including managing embeddings and vector search. - Strong understanding of transformer architectures, tokenization, and prompt engineering techniques. - Comfortable working with LLM APIs (e.g., OpenAI, Anthropic, Cohere) and serving models with FastAPI, Flask, or similar frameworks. - Familiarity with deploying ML systems using Docker, Kubernetes, and cloud services (AWS, GCP, Azure). - Experience with model evaluation, logging, and inference pipeline troubleshooting. Nice to Have: - Exposure to multimodal models (e.g., text-to-image, video generation, TTS). - Experience with reinforcement learning from human feedback (RLHF) or alignment techniques. - Familiarity with open-source LLMs (e.g., Mistral, Mixtral, LLaMA, Falcon) and optimization tools (LoRA, quantization, PEFT). - Knowledge of LangChain agents, tool integration, and memory management. - Contributions to open-source GenAI projects, public demos, or blogs in the generative AI space. - Basic proficiency in frontend development (e.g., React, Next.js) for rapid prototyping.

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