Data Science Senior Architect

Key Responsibilities

GenAI Solution Architecture & Design

1. Lead the design and development of scalable GenAI solutions leveraging LLMs, diffusion models, and multimodal architectures.
2. Architect end-to-end pipelines involving prompt engineering, vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning.
3. Select and integrate foundational models (e.g., GPT, Claude, LLaMA, Mistral) based on business needs and technical constraints.

Technical Strategy & Leadership

1. Define GenAI architecture blueprints, best practices, and reusable components for rapid development and experimentation.
2. Guide teams on model evaluation, inference optimization, and cost-effective scaling strategies.
3. Stay current on the rapidly evolving GenAI landscape and assess emerging tools, APIs, and frameworks.

Collaboration & Delivery

1. 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.
2. Support PoCs, pilots, and production deployments of GenAI models in secure, compliant environments.
3. Collaborate with MLOps and cloud teams to enable continuous delivery, monitoring, and governance of GenAI systems.

Required Qualifications & Experience

1. Education: Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related technical field. PhD is a plus.
2. Experience: 12–15 years in AI/ML and software engineering, with 3+ years focused on Generative AI and LLM-based architectures.

Skills

Core Skills

1. Deep expertise in machine learning, natural language processing (NLP), and deep learning architectures.
2. Hands-on experience with LLMs, transformers, fine-tuning techniques (LoRA, PEFT), and prompt engineering.
3. Proficient in Python, with libraries/frameworks such as Hugging Face Transformers, LangChain, OpenAI API, PyTorch, TensorFlow.
4. Experience with vector databases (e.g., Pinecone, FAISS, Weaviate) and RAG pipelines.
5. Strong understanding of cloud-native AI architectures (AWS/GCP/Azure), containerization (Docker/Kubernetes), and API integration.

Architectural & Leadership Skills

1. Proven ability to design and deliver scalable, secure, and efficient GenAI systems.
2. Strong communication skills for cross-functional collaboration and stakeholder engagement.
3. Ability to mentor engineering teams and drive innovation across the AI/ML ecosystem.

Nice-to-Have

1. Experience with multimodal models (text + image/audio/video).
2. Knowledge of AI governance, ethical AI, and compliance frameworks.
3. Familiarity with MLOps practices for GenAI, including model versioning, drift detection, and performance monitoring.