<p><p><b>Job Title : </b> Python Developer GenAI/LLM (C2H Position)<br/><br/><b>Experience : </b> 5+ Years<br/><br/><b>Work Mode : </b> Remote / Hybrid (as per client the Role :</b></p><p><br/></p><p>We are seeking an experienced Python Developer with strong expertise in Generative AI (GenAI), Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to join our team on a Contract-to-Hire (C2H) basis.
The ideal candidate will have hands-on experience in developing and deploying AI/ML-based applications, integrating advanced language models, and building innovative solutions.</p><p>Please apply only if you are open to a C2H Responsibilities :</b></p><p><br/></p>- Design, develop, and optimize Python-based applications for AI/ML solutions.<br/><br/></p><p>- Implement RAG pipelines and integrate LLMs for enterprise use cases.<br/><br/></p><p>- Work with vector databases and embeddings to enable contextual AI solutions.<br/><br/></p><p>- Fine-tune and deploy pre-trained models for specific business needs.<br/><br/></p><p>- Build APIs and services around AI/ML solutions for seamless integration.<br/><br/></p><p>- Collaborate with data scientists, ML engineers, and product teams to deliver scalable AI applications.<br/><br/></p><p>- Stay updated with the latest advancements in GenAI, prompt engineering, and model Skills :</b></p><p><br/></p>- Strong programming expertise in Python.<br/><br/></p><p>- Hands-on experience with Generative AI, LLMs (OpenAI, Anthropic, Hugging Face, etc.).<br/><br/></p><p>- Knowledge of RAG workflows, embeddings, and vector databases (Pinecone, FAISS, Weaviate, etc.).<br/><br/></p><p>- Experience in AI/ML frameworks (PyTorch, TensorFlow, LangChain, LlamaIndex).<br/><br/></p><p>- Familiarity with cloud platforms (Azure, AWS, or GCP) for model deployment.<br/><br/></p><p>- Strong understanding of APIs, microservices, and containerization (Docker/Kubernetes).<br/><br/></p><p>- Excellent problem-solving, analytical, and communication to Have :</b></p><p><br/></p>- Experience with fine-tuning and training LLMs.<br/><br/></p><p>- Exposure to MLOps and model monitoring.<br/><br/></p><p>- Knowledge of prompt engineering techniques.</p><br/></p> (ref:hirist.tech)