Data Scientist - Generative AI

Job Title : Data Scientist Gen AI

Experience : 7-13years

Location : Bangalore, Pune, Chennai, Kolkata, Gurugram

Work Mode : Hybrid

Notice Period : Immediate (15 days)

Responsibilities :

- 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.