AI Architect

Daman; United States; On-site; USD 150,000 - 210,000; Full time

Job summary

Daman is seeking an experienced AI Architect to design autonomous AI solutions leveraging Agentic AI, LLMs, and multi-agent orchestration. You will guide engineering teams and ensure scalable deployments across enterprise environments.

Responsibilities include defining LLM-powered architectures, integrating with enterprise systems, and leading AI pipelines with governance and observability practices. Collaboration with product and data science teams is essential.

Qualifications

• Bachelor’s or Master’s degree in CS, AI, or data science.
• 8+ years software engineering or AI architecture experience.
• 3+ years with LLM-based systems and generative AI.
• Proficiency with Python and AI/ML frameworks.
• Hands-on experience with LLM orchestration frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel).
• Experience with vector databases (Pinecone, Weaviate, FAISS).
• Knowledge of RAG architectures, prompts, tool integration, and memory management in AI agents.
• Experience deploying AI workloads in Azure, AWS, or GCP.
• Strong API design, microservices, and distributed systems knowledge.

Responsibilities

• Design Agentic AI systems with autonomous reasoning and task execution.
• Build multi-agent frameworks to solve complex workflows.
• Define architecture for LLM-powered apps including prompt orchestration and memory management.
• Integrate AI agents with enterprise APIs and data platforms.
• Lead AI pipelines, orchestration layers, and evaluation frameworks.
• Establish governance, safety, observability, and monitoring practices.
• Collaborate with product, data science, and engineering teams to translate requirements.
• Guide teams deploying scalable AI solutions on major cloud platforms (Azure, AWS, GCP).
• Evaluate new technologies in autonomous AI and agent frameworks.
• Provide technical leadership and mentorship to AI/ML engineers.

Skills

AI architecture

Agentic AI

Multi-agent systems

Technical leadership

Cloud understanding

Education

Bachelor’s or Master’s in CS/AI

Tools

LangChain

AutoGen

CrewAI

Semantic Kernel

Pinecone

Weaviate

FAISS

Job description

We are seeking an experienced AI Architect with strong expertise in Agentic AI systems to design and implement intelligent, autonomous AI solutions that can reason, plan, and execute complex tasks. The ideal candidate will have deep experience in large language models (LLMs), multi-agent systems, orchestration frameworks, and AI infrastructure, along with the ability to translate business requirements into scalable AI architectures.

This role will involve architecting end-to-end AI solutions, guiding engineering teams, and ensuring reliable deployment of agent-based AI systems across enterprise environments.

Key Responsibilities

• Design and architect Agentic AI systems capable of autonomous reasoning, decision-making, and task execution.
• Build and implement multi-agent frameworks that collaborate to solve complex workflows.
• Define architecture for LLM-powered applications, including prompt orchestration, tool usage, and memory management.
• Integrate AI agents with enterprise systems, APIs, and data platforms.
• Lead the design of AI pipelines, orchestration layers, and evaluation frameworks.
• Establish best practices for AI governance, safety, observability, and monitoring.
• Collaborate with product managers, data scientists, and engineering teams to translate business requirements into AI-driven solutions.
• Guide teams in deploying scalable AI solutions on cloud platforms such as Azure, AWS, or GCP.
• Evaluate and adopt emerging technologies in autonomous AI, reasoning systems, and agent frameworks.
• Provide technical leadership and mentorship to AI/ML engineers.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
• 8+ years of experience in software engineering, machine learning, or AI architecture.
• 3+ years of experience working with LLM-based systems and generative AI solutions.
• Proficiency with Python and AI/ML frameworks.
• Hands-on experience with LLM orchestration frameworks such as LangChain, AutoGen, CrewAI, Semantic Kernel, or similar.
• Experience working with vector databases (Pinecone, Weaviate, FAISS, etc.).
• Knowledge of RAG architectures, prompt engineering, tool integration, and memory management in AI agents.
• Experience deploying AI workloads in cloud environments (Azure, AWS, or GCP).
• Strong understanding of API design, microservices architecture, and distributed systems.