AI Architect

AI Architect

Primary Stack & Tools:

• Languages

  : Python, SQL, Bash

• ML/AI Frameworks

  : PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers

• GenAI & LLM Tooling

  : OpenAI APIs, LangChain, LlamaIndex, Cohere, Claude, Azure OpenAI

• Agentic & Multi-Agent Frameworks

  : LangGraph, CrewAI, Agno, AutoGen

• Search & Retrieval

  : FAISS, Pinecone, Weaviate, Elasticsearch

• Cloud Platforms

  : AWS, GCP, Azure (preferred: Vertex AI, SageMaker, Bedrock)

• MLOps & DevOps

  : MLflow, Kubeflow, Docker, Kubernetes, CI/CD pipelines, Terraform, FAST API

• Data Tools

  : Snowflake, BigQuery, Spark, Airflow

Key Responsibilities:

• Architect scalable and secure AI systems leveraging

  LLMs

  ,

  GenAI

  , and

  multi-agent frameworks

  to support diverse enterprise use cases (e.g., automation, personalization, intelligent search).
• Design and oversee implementation of

  retrieval-augmented generation (RAG)

  pipelines integrating vector databases, LLMs, and proprietary knowledge bases.
• Build robust

  agentic workflows

  using tools like

  LangGraph

  ,

  CrewAI

  , or

  Agno

  , enabling autonomous task execution, planning, memory, and tool use.
• Collaborate with product, engineering, and data teams to translate business requirements into architectural blueprints and technical roadmaps.
• Define and enforce

  AI/ML infrastructure best practices

  , including security, scalability, observability, and model governance.
• Manage technical road-map, sprint cadence, and 3–5 AI engineers; coach on best practices.
• Lead AI solution design reviews and ensure alignment with compliance, ethics, and responsible AI standards.
• Evaluate emerging GenAI & agentic tools; run proofs-of-concept and guide build-vs-buy decisions.

Qualifications:

• 10+ years of experience in AI/ML engineering or data science, with 3+ years in AI architecture or system design.
• Proven experience designing and deploying

  LLM-based solutions

  at scale, including

  fine-tuning

  ,

  prompt engineering

  , and

  RAG-based systems

  .
• Strong understanding of

  agentic AI design principles

  ,

  multi-agent orchestration

  , and

  tool-augmented LLMs

  .
• Proficiency with cloud-native ML/AI services and infrastructure design across AWS, GCP, or Azure.
• Deep expertise in model lifecycle management, MLOps, and deployment workflows (batch, real-time, streaming).
• Familiarity with

  data governance

  ,

  AI ethics

  , and

  security considerations

  in production-grade systems.
• Excellent communication and leadership skills, with the ability to influence technical and business stakeholders.