Data Science Senior Architect
- Bangalore, Karnataka
- Permanent
- Full-time
- 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, Mistral) based 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.
- Education: Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related technical field. PhD is a plus.
- Experience: 12–15 years in AI/ML and software engineering, with 3+ years focused on Generative AI and LLM-based architectures.
- 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.
- Proven ability to design and deliver scalable, secure, and efficient GenAI systems.
- Strong communication skills for cross-functional collaboration and stakeholder engagement.
- Ability to mentor engineering teams and drive innovation across the AI/ML ecosystem.
- 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.