Key Responsibilities

A. Agent Design & Development

• Design and build AI agents that plan and execute multi-step tasks using tool calling, structured outputs,

and state management.

• Build multi-agent systems — orchestrator/worker, planner/executor, and review patterns — and decide

when a single agent is the better choice.

• Translate business processes from BA and solution documents into agent workflows with clear goals,

steps, decision points, and exit conditions.

• Design agent memory and context: conversation state, long-running task state, and retrieval of relevant

knowledge at each step.

B. Tools, Integrations & MCP

• Build and maintain the tools agents use — API connectors, database queries, document actions, and

browser or desktop automation where no API exists.

• Develop Model Context Protocol (MCP) servers that expose client systems to agents in a secure, reusable

way.

• Integrate agents with enterprise platforms such as Salesforce, ServiceNow, SAP, Microsoft 365, Google

Workspace, and telephony systems.

• Write clear tool descriptions, input schemas, and error messages so agents use tools correctly and recover

when a call fails.

C. Reliability, Safety & Evaluation

• Build evaluation suites for agent behaviour: task success rate, tool-selection accuracy, step count, cost per

task, and failure analysis on real traces.

• Design human-in-the-loop checkpoints for high-impact actions — approvals, escalations, and handoff to

human agents.

• Enforce guardrails: least-privilege tool access, prompt injection defences, action limits, timeouts, and safe

fallbacks.

• Instrument full traceability — log every model call, tool call, and decision so any agent run can be replayed

and audited.

• Control cost and latency through model routing, caching, context management, and limits on runaway

loops.

D. Deployment & Collaboration

• Deploy agents as reliable services — containerised, observable, with queueing and retry handling for longrunning tasks.

• Prepare demo-ready agent workflows for client pitches, working with the AI Solutions Lead on scenarios

that show clear business value.

• Contribute reusable agent templates, tool libraries, and MCP connectors to Flatworld's AI accelerator

library.

• Document agent architecture, tool inventories, permissions, and known limitations for every build.

Requirements

Mandatory Technical Requirements

The following are non-negotiable for this role:

• Python: Strong production-grade Python — clean, typed, tested code; async programming; API

integration. [MANDATORY]

• LLM Tool Calling: Hands-on experience building applications with LLM tool/function calling and structured

outputs on at least one major API (Anthropic, OpenAI, Google, or Azure OpenAI). [MANDATORY]

• Agent Frameworks: Built at least one working agent with LangGraph, CrewAI, AutoGen, OpenAI Agents

SDK, Claude Agent SDK, or a well-structured custom orchestration loop. [MANDATORY]

• System Integration: Experience integrating with REST APIs, webhooks, and authentication schemes

(OAuth, API keys, service accounts). [MANDATORY]

• Agent Evaluation & Tracing: Practice of testing agent behaviour with scenario-based test sets and

inspecting execution traces — not just manual trial runs. [MANDATORY]

• Deployment: Ability to containerise with Docker and deploy services to a cloud platform; proficiency with

Git. [MANDATORY]

Strongly Preferred

• Model Context Protocol (MCP): Building or deploying MCP servers and clients.

• RAG: Retrieval pipelines and vector databases (Pinecone, Qdrant, Chroma, or pgvector) used as agent

knowledge sources.

• Observability: Agent tracing and evaluation tools such as LangSmith, Langfuse, Arize Phoenix, or

OpenTelemetry.

• Workflow & Queues: Durable workflow or task-queue tools (Temporal, Celery, Redis, or cloud-native

equivalents) for long-running agent tasks.

• Cloud Agent Platforms: AWS Bedrock Agents, Azure AI Foundry Agent Service, or Google Vertex AI Agent

Builder.

Advantageous

Not required, but a clear differentiator for this role:

• Voice agents — real-time speech-to-text, text-to-speech, and telephony integration for contact centre

automation.

• Browser or computer-use agents (Playwright, Selenium, or model-native computer use) for systems

without APIs.

• Background in RPA (UiPath, Automation Anywhere, Power Automate) and moving rule-based bots to

Agentic workflows.

• Experience with enterprise platforms such as SAP, IBM Maximo, or ServiceNow.

• Awareness of data protection requirements (GDPR, HIPAA, India's DPDP Act) as they apply to autonomous

systems.

Benefits

What We Offer

• Work at the front of applied AI — building agents that automate real enterprise processes, not just chat

interfaces.

• Variety across industries and systems, with a direct line from your build to a client decision.

• Access to current commercial and open-weight models and freedom to choose the right framework for

each problem.

• Mentorship from the AI Solutions Lead and AI Solution Architect, with exposure to pre-sales and solution

design.

• Learning budget for AI upskilling, conferences, and cloud certifications.

• Competitive compensation with a clear path toward Senior Agentic AI Engineer or AI Solution Architect

tracks

Agentic AI Engineer

About the role

Skills

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Requirements

Benefits