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