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AI Systems Engineer

Michael Page

Bengaluru

3-5 years

Today

$26.5K–41.0K/yr

Full-time

Onsite

Skills Required

LLM

RAG

LangChain

LlamaIndex

Agentic AI

Prompt Engineering

Vector Database

Embeddings retrieval optimization

LangGraph

PromptOps

Python

CrewAI reasoning tool execution

Description

Lead the architecture and development of a next-generation enterprise AI agent. The role focuses on AI quality, governance and AgentOps standards for enterprise productivity intelligence.

Company: Michael Page

Role: AI Systems Engineer

Location: Bangalore

Experience

- 3 to 5 years of software engineering or AI product development experience
- At least 2 years building and deploying production-grade LLM applications

Responsibilities

- Lead the architecture and development of a next-generation enterprise AI agent
- Design multi-agent workflows with orchestration frameworks
- Build end-to-end agent pipelines spanning prompting, retrieval, reasoning and tool execution
- Develop advanced prompt engineering frameworks, structured outputs and PromptOps practices
- Create scalable RAG systems for enterprise telemetry, benchmarks and operational data
- Own vector search, embedding pipelines, retrieval quality and context-grounding strategies
- Establish AI evaluation frameworks, regression testing, observability and quality gates
- Partner with product, engineering and data science teams to deliver trusted AI capabilities

Additional Responsibilities

- Shape AI quality, governance and AgentOps standards




- Work on a flagship enterprise AI agent from the ground up

Nice To Have

- Experience with LangGraph or CrewAI
- Experience with LangChain, LlamaIndex or similar frameworks
- Focus on reliability, evaluation, governance and production operations
- Exposure to agentic AI, productivity governance and enterprise analytics
- Collaboration with senior leaders across research, product and engineering
- Ownership of a strategically important AI initiative with visible business impact
- Prospect to solve complex enterprise AI and productivity intelligence challenges
- High autonomy and influence over architecture, engineering standards and AI quality

More Skills multi-agent workflows, agentic systems, multi-step reasoning, evaluation frameworks, regression testing, observability, quality gates, AI application architecture

Prepare for this role

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