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.
- Solid 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

📌 AI Solution Architect / AI Solutions Lead – Agentic AI (Bengaluru)
🏢 Three Across
📍 Bengaluru