AI Agent Platform Engineer

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Role Overview

AI Agent Framework

Key Responsibilities

1. Agent Architecture & Runtime

• Architect and implement the core orchestration engine for AI agents (event-driven/task-based)
• Manage agent lifecycle functions such as spawn, pause, escalate, and terminate
• Enable secure, real-time communication between agents, services, and workflows
• Integrate memory and retrieval systems using vector databases like Pinecone, Weaviate, or Qdrant

2. LLM Integration & Prompt Engineering

• Integrate LLM providers (OpenAI, Azure OpenAI, Anthropic, Mistral, etc.) into agent workflows
• Create modular prompt templates with retry/fallback mechanisms
• Implement chaining logic and dynamic tool use for agents using LangChain or LlamaIndex
• Develop reusable agent types such as Summarizer, Validator, Notifier, Planner, etc.

3. Backend API & Microservices Development

• Develop FastAPI-based microservices for agent orchestration and skill execution
• Create APIs to register agents, execute agent actions, and manage runtime memory
• Implement RBAC, rate limiting, and security protocols for multi-tenant deployments

4. Data Integration & Task Routing

• Build connectors to integrate structured (CRM, SQL) and unstructured data sources (email, docs, transcripts)
• Route incoming data streams to relevant agents based on workflow and business rules
• Support ingestion from tools like Salesforce, HubSpot, Gong, and Zoom

5. DevOps, Monitoring, and Scaling

• Deploy the agent platform using Docker and Kubernetes on Azure
• Implement Redis, Celery, or equivalent async task systems for agent task queues
• Set up observability to monitor agent usage, task success/failure, latency, and hallucination rates
• Create CI/CD pipelines for agent modules and prompt updates

Ideal Candidate Profile

• 2–4 years of experience in backend engineering, ML engineering, or agent orchestration
• Strong command over Python (FastAPI, asyncio, Celery, SQLAlchemy)
• Experience with LangChain, LlamaIndex, Haystack, or other orchestration libraries
• Hands-on with OpenAI, Anthropic, or similar LLM APIs
• Comfortable with vector embeddings and semantic search systems
• Understanding of modern AI agent frameworks like AutoGen, CrewAI, Semantic Planner, or ReAct
• Familiarity with multi-tenant API security and SaaS architecture
• Bonus: Frontend collaboration experience to support UI for agents and dashboards
• Bonus: Familiarity with SaaS platforms in B2B domains like RevOps, CRM, or workflow automation

What You’ll Gain

• Ownership of agent architecture inside a live enterprise-grade AI platform
• Opportunity to shape the future of AI-first business applications
• Collaboration with founders, product leaders, and early enterprise customers
• Competitive salary with potential ESOP
• First-mover engineering credit on one of the most advanced automation stacks in SaaS