AI Solution Architect / AI Solutions Lead Agentic AI

Role Overview

We are looking for an experienced AI Solution Architect / AI Solutions Lead with approximately 10 years of overall experience and a strong 6+ year foundation in Machine Learning / AI.

The ideal candidate will have evolved from a core ML/AI engineering background into Generative AI and Agentic AI, with hands-on experience designing, building, deploying, securing, and operating production-grade AI agents and multi-agent systems.

Candidates whose AI experience started primarily with LLMs/GenAI and who lack substantial prior ML experience should be excluded.

Key Responsibilities

• Design end-to-end AI/ML and Agentic AI solutions for enterprise use cases.
• Architect and implement AI agents, multi-agent systems and agentic workflows.
• Define agent architecture covering:
• Agent orchestration
• Tool/function calling
• Planning and reasoning
• Memory/context management
• Routing and handoffs
• Multi-agent collaboration
• Human-in-the-loop mechanisms
• Guardrails and evaluation
• Apply appropriate agentic design patterns such as manager/sub-agent, handoff, router, sequential/chained workflows and parallel agent execution.
• Design for security, reliability, scalability, observability and operationalization of AI agents.
• Develop and productionize AI solutions using Python as the primary programming language.
• Integrate LLMs, ML models, APIs, enterprise data sources and external tools into agentic applications.
• Evaluate and select appropriate AI/ML models, agent frameworks and orchestration approaches.
• Build reusable frameworks/components for enterprise AI solutions.
• Work closely with engineering, data, product and business teams to translate requirements into scalable AI architectures.
• Establish evaluation, monitoring, tracing and performance mechanisms for production AI/agent systems.

Mandatory Technical Skills

AI / ML

• 10+ years overall technology experience.
• 6+ years of strong AI/ML experience mandatory.
• Strong foundation in traditional Machine Learning, including model development, training, evaluation and deployment.
• Experience with areas such as supervised/unsupervised learning, NLP, recommendation systems, predictive modelling, deep learning, etc.
• Strong understanding of ML lifecycle / MLOps.
  
  

Generative AI / Agentic AI

• Strong hands-on experience building AI Agents / Agentic AI systems.
• Deep understanding of Agentic Architecture and Design Patterns.
• Experience with:
• Multi-agent architectures
• Agent orchestration
• Tool/function calling
• Agent routing
• Handoffs
• Agent memory/context
• RAG
• Guardrails
• Evaluation
• Observability
• Human-in-the-loop
• Strong understanding of security and operational considerations for AI agents.
  

Agent Development SDKs / Frameworks

Hands-on Experience With One Or More Of

• OpenAI Agents SDK / OpenAI APIs
• Anthropic
• LangChain
• LangGraph
• Other equivalent agent orchestration frameworks
  
  

OpenAI's current Agents SDK, for example, supports agents, tools, handoffs, guardrails, sessions, tracing and multi-agent orchestration useful indicators of the depth expected for this role.

Python Full Stack

• Strong Python development skills.
• Ability to build AI applications end-to-end rather than only developing models.
• Experience with APIs, backend services, integrations and application architecture.
• Experience building production-grade AI/ML applications using Python frameworks.
• Good understanding of databases, cloud services, APIs and distributed application architecture