Lead I - ML Engineering

• Design and build AI agents that automate meaningful end-to-end workflows
• Engineer agent body components including:
  • enterprise connectors and APIs
  • tool calling layers
  • persistent memory and session/state handling
  • context management, retrieval, and skills/pattern libraries
  • orchestration across single-agent and multi-agent flows
  • evaluation, tracing, and guardrails
• Build connectors to enterprise platforms, data stores, documents, and operational systems using standardized tool and protocol patterns where appropriate, including MCP-style integrations. MCP is an open protocol for exposing tools, resources, and prompts to LLM applications.
• Create robust cloud-native services to support agent execution, logging, observability, access control, and controlled deployments.
• Implement persistent and short-term memory patterns so agents can retain useful context without becoming noisy or brittle. OpenAI s current guidance explicitly highlights sessions, trimming, and compression as important context-engineering patterns for long-running agents.
• Build and refine workflows for agent handoffs, tool selection, safe action execution, and human-in-the-loop approval where required. Both OpenAI and Anthropic now describe simple, composable agent workflows and tool-based systems as the practical path to effective agents.
• Prototype quickly, test aggressively, and move the strongest agent patterns into stable internal use.
• Use AI directly in your own engineering workflow to accelerate requirements interpretation, system design, coding, testing, debugging, documentation, and iteration.

Required Qualifications

• Strong experience in software engineering, with a track record of building production-quality systems.
• Demonstrated hands-on experience building AI agents or agentic applications with tools, workflows, orchestration, or autonomous execution patterns.
• Strong experience with Python and/or TypeScript for backend or agent-system development.
• Experience with cloud infrastructure and modern deployment patterns across one or more major cloud platforms.
• Experience building connectors to APIs, databases, file systems, enterprise tools, or SaaS products.
• Experience with memory/state handling, context engineering, retrieval, or knowledge-grounded agent behavior. Persistent context and memory management are now treated as core implementation concerns in production agent systems.
• Experience with observability, debugging, tracing, or evaluation of complex application behavior. Agent tracing is now a formal capability in current agent SDKs.
  • Strong judgment in separating what should be handled by prompts, retrieval, deterministic code, memory, or workflow design.