AI Architect

Position

Experience

Employment Type

About the Role

AI Architect

Key Responsibilities

• Architecture & Design

• Define end-to-end architecture for

  GenAI & Agentic AI solutions

  .
• Design scalable and secure AI pipelines including

  data ingestion, model training, fine-tuning, deployment, and monitoring

  .
• Architect multi-agent AI systems leveraging frameworks like

  LangChain, AutoGen, LlamaIndex, or similar

  .

• Hands-on Development

• Build and optimize AI applications in

  Python

  using frameworks such as

  PyTorch, TensorFlow, Hugging Face Transformers

  .
• Implement and integrate

  LLMs, embeddings, RAG (Retrieval-Augmented Generation), vector databases (Pinecone, FAISS, Weaviate, Milvus)

  .
• Develop

  autonomous AI agents

  for workflow automation, reasoning, and decision-making.

• Innovation & Strategy

• Evaluate and recommend emerging

  GenAI & Agentic AI frameworks, tools, and platforms

  .
• Define best practices for

  prompt engineering, model fine-tuning, safety, compliance, and governance

  .
• Collaborate with stakeholders to align AI solutions with

  business goals and enterprise architecture

  .

• Leadership & Mentoring

• Lead technical discussions and provide architectural guidance to engineering teams.
• Mentor data scientists, ML engineers, and software developers in AI best practices.
• Collaborate with product managers and business leaders on solution roadmaps.

Required Skills & Experience

• 10–12 years

  of experience in software engineering / AI solutions, with at least

  4+ years in AI/ML and GenAI

  .
• Strong expertise in

  Python

  and AI/ML frameworks (

  PyTorch, TensorFlow, Hugging Face

  ).
• Proven experience architecting

  Generative AI solutions

  (LLMs, diffusion models, multimodal AI).
• Hands-on experience with

  agent frameworks

  (LangChain, AutoGen, CrewAI, etc.) for building

  Agentic AI systems

  .
• Expertise in

  vector databases

  (Pinecone, FAISS, Milvus, Weaviate) and

  RAG architectures

  .
• Strong understanding of

  cloud platforms

  (AWS, Azure, GCP) and AI services (Bedrock, Vertex AI, OpenAI APIs).
• Knowledge of

  MLOps practices

  : CI/CD pipelines, monitoring, observability, and governance for AI systems.
• Experience with

  scalable distributed systems

  , API integration, and microservices architecture.
• Familiarity with

  data privacy, security, and ethical AI practices

  .
• Familiarity working with healthcare domain would be an added advantage

Preferred Skills

• Experience with

  multi-modal GenAI models

  (text, vision, speech).
• Knowledge of

  enterprise AI platforms

  (Databricks, Snowflake, Azure AI, AWS SageMaker).
• Contributions to

  open-source AI projects

  .
• Strong background in

  solution architecture

  and

  enterprise AI adoption strategies

  .

Education

• Bachelor’s/Master’s degree in

  Computer Science, AI/ML, Data Science, or related field

  .
• Certifications in

  AI/ML, cloud architecture, or Generative AI

  are a plus.