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

Work arrangement
No