AI Architect

Responsibilities

• Agentic AI Development:

agentic AI systems

multi-agent systems

• RAG Pipelines:

retrieval-augmented generation (RAG)

• LLM Engineering:

task-specific reasoning, planning, and dynamic adaptation

LLM/SLM APIs, embeddings, and advanced generative AI techniques

• Enterprise AI Platform:

LLMs, RAG, embeddings, and agentic AI protocols

Model Context Protocol (MCP)

• MLOps & Observability:

MLOps, monitoring, and observability

• Applied AI Prototyping:

prototype, experiment, and iterate

• Collaboration & Research:

full research cycle

Collaborate effectively with other engineers, researchers, and data scientists.

documentation and standardization

Required Education

Bachelors degree in Computer Science, Engineering, or a related quantitative field. Master’s or Ph.D. is a strong plus.

Required Experience

• 10 plus years overall experience

  in software development, data science, or machine learning.

• 3 + year of hands-on experience

  developing AI applications with LLMs and systems such as

  retrieval-based methods, fine-tuning, or agent-based architectures

  .

• 3+ year of experience

  with frameworks like

  LangChain, LlamaIndex, OpenAI, or similar tools

  .

Required Technical Skills

• Strong programming skills in

  Python

  and basics in

  SQL

  .
• Expertise with

  LLM/SLM APIs, embeddings, and RAG systems

  .
• Experience deploying on

  Google Cloud Platform (GCP)

  with

  Vertex AI,

  and

  IBM WatsonX

  .
• Familiarity with

  agentic AI protocols

  and exposure to

  Agent Development Kits (ADKs)

  .

Required Other Skills

• Technical Knowledge of AI
• Foundational Architecture
• Enterprise Solution Perspective
• Negotiation and Trade-Offs
• Strategic Planning & Business Acumen
• Problem-Solving
• Adaptability
• Communication Clarity
• Roadmap & Strategy

Preferred Qualifications

• Experience implementing

  Model Context Protocol (MCP)

  for agent coordination.
• Prior exposure to

  LangGraph, AutoGen, or related orchestration frameworks

  .
• Knowledge of

  MLOps best practices

  (CI/CD for ML, observability, monitoring, scaling).
• Familiarity with

  responsible AI

  principles (safety, fairness, interpretability).
• Experience in

  enterprise-scale deployments

  of AI-driven platforms.
• Contributions to

  open-source AI/ML projects

  are a plus.