Data Science Senior Architect

About the Role

Generative AI Architect

As the GenAI Architect, you will work closely with data scientists, ML engineers, product teams, and stakeholders to conceptualize, prototype, and scale generative AI use cases across the organization or client engagements.

Key Responsibilities

GenAI Solution Architecture & Design

• 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.

Technical Strategy & Leadership

• 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.

Collaboration & Delivery

• 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.

Required Qualifications & Experience

• 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

  .

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.

Architectural & Leadership Skills

• 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.

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.