Agentic AI Engineer

Flatworld Solutions Pvt Ltd.; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Benefits offered by this job

Mentorship from AI Solutions Lead

Learning budget for upskilling

Career growth path

Job summary

Flatworld Solutions Pvt Ltd. in Bengaluru is seeking a Senior AI Engineer to design and build AI agents that plan and execute multi-step tasks, using tool calling and structured outputs.

You will implement multi-agent architectures, ensure state management, and translate business processes into agent workflows with clear goals and exit conditions. The role emphasizes deployment, observability, and collaboration with an AI Solutions Lead, offering mentorship and a strong growth path.

Qualifications

• Strong production-grade Python with async programming and API integration.
• Hands-on experience building applications with LLM tool/function calling and structured outputs on major APIs.
• Built at least one working agent with LangGraph, CrewAI, AutoGen, OpenAI Agents SDK, Claude Agent SDK, or a structured custom loop.
• Experience integrating with REST APIs, webhooks, and authentication schemes (OAuth, API keys).
• Practice of testing agent behaviour with scenario-based test sets and inspecting execution traces.

Responsibilities

• Design and build AI agents that plan and execute multi-step tasks using tool calling and structured outputs.
• Build multi-agent systems with orchestrator/worker, planner/executor, and review patterns; decide when a single agent suffices.
• Translate business processes from BA/solution docs into agent workflows with clear goals, steps, decision points, exit conditions.
• Design agent memory and context: conversation state, long-running task state, and knowledge retrieval at each step.
• Deploy agents as reliable services—containerised, observable, with queueing and retry handling for long-running tasks.

Skills

Python

LLM Tool Calling

Agent Frameworks

System Integration

Agent Evaluation & Tracing

Git

Docker

Tools

LangGraph

CrewAI

AutoGen

OpenAI Agents SDK

Claude Agent SDK

Docker

Job description

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

• 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