Agentic AI Engineer

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