Job Description
<p><b>Job Title : Data Scientist Gen AI</b><br/><br/><b>Experience : 7-13years</b><br/><br/><b>Location : Bangalore, Pune, Chennai, Kolkata, Gurugram</b><br/><br/><b>Work Mode : Hybrid</b><br/><br/><b>Notice Period : Immediate (15 :</b><b></b></p><p class=""><br/>- Lead the design and development of scalable GenAI solutions leveraging LLMs, diffusion models, and multimodal architectures.<br/><br/>- Architect end-to-end pipelines involving prompt engineering, vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning.<br/><br/>- Select and integrate foundational models (e.
g., GPT, Claude, LLaMA, Mistralbased on business needs and technical constraints.<br/><br/>- Define GenAI architecture blueprints, best practices, and reusable components for rapid development and experimentation.<br/><br/>- Guide teams on model evaluation, inference optimization, and cost-effective scaling strategies.<br/><br/>- Stay current on the rapidly evolving GenAI landscape and assess emerging tools, APIs, and frameworks.<br/><br/>- Work with product owners, business leaders, and data teams to identify high-impact GenAI use cases across domains like customer support, content generation, document understanding, and code generation.<br/><br/>- Support PoCs, pilots, and production deployments of GenAI models in secure, compliant environments.<br/><br/>- Collaborate with MLOps and cloud teams to enable continuous delivery, monitoring, and governance of GenAI systems.<br/><br/><b>Core Skills :</b><br/><br/>- Deep expertise in machine learning, natural language processing (NLP), and deep learning architectures.<br/><br/>- Hands-on experience with LLMs, transformers, fine-tuning techniques (LoRA, PEFT), and prompt engineering.<br/><br/>- Proficient in Python, with libraries/frameworks such as Hugging Face Transformers, LangChain, OpenAI API, PyTorch, TensorFlow.<br/><br/>- Experience with vector databases (e.
g., Pinecone, FAISS, Weaviate) and RAG pipelines.<br/><br/>- Strong understanding of cloud-native AI architectures (AWS/GCP/Azure), containerization (Docker/Kubernetes), and API integration.<br/><br/><b>Nice-to-Have :</b><br/><br/>- Experience with multimodal models (text + image/audio/video).<br/><br/>- Knowledge of AI governance, ethical AI, and compliance frameworks.<br/><br/>- Familiarity with MLOps practices for GenAI, including model versioning, drift detection, and performance monitoring.</p> (ref:hirist.tech)