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
