Solution Architect - Gen AI

About the Role:

Generative AI Architect

Key Responsibilities:

• Define and own the

  architecture strategy

   for Generative AI and LLM-based solutions across multiple business domains.
• Evaluate, select, and integrate

  AI frameworks, models, and platforms

   (e.g., OpenAI, Azure OpenAI, Vertex AI, Claude, Llama, etc.).
• Architect and deploy

  end-to-end AI pipelines

  , including data ingestion, model training, fine-tuning, and inference.
• Lead

  RAG (Retrieval-Augmented Generation)

  ,

  prompt engineering

  , and

  context management

   design for enterprise-grade solutions.
• Collaborate with

  data engineering teams

   to design scalable data architectures that support AI workloads.
• Drive

  POCs and pilot projects

   to assess model performance, latency, and cost efficiency.
• Ensure compliance with

  AI governance, ethics, and data privacy

   standards.
• Mentor and guide engineering teams in best practices for

  AI system design, MLOps, and deployment.

• Stay abreast of emerging trends in Generative AI, deep learning, and NLP to continuously enhance solution design.

Required Skills & Qualifications:

• Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.

• 12–15 years of total experience

   in software development, architecture, or AI engineering — with

  at least 3–5 years in AI/ML solution design.

• Proven expertise in

  Generative AI, NLP, and LLM-based architectures.

• Hands-on experience with

  transformer models

   (e.g., GPT, Llama, Claude, Mistral, etc.) and frameworks such as

  LangChain, LlamaIndex, Hugging Face Transformers.

• Strong proficiency in

  Python

  , with exposure to

  PyTorch, TensorFlow

  , or

  JAX.

• Experience integrating AI systems with

  cloud platforms

   —

  AWS Sagemaker

  ,

  Azure AI

  , or

  Google Vertex AI.

• Familiarity with

  vector databases

   (Pinecone, FAISS, Chroma, Milvus) and

  embedding models.

• Understanding of

  microservices architecture

  ,

  APIs

  , and

  containerization (Docker/Kubernetes).

• Excellent understanding of

  data engineering, model lifecycle management, and MLOps practices.

• Strong leadership, communication, and stakeholder management skills.