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Salary: USD 179,400 - 248,120 / annual
6sense is building production AI agents and an MCP (Model Context Protocol) server used by real customers today. This Senior AI Engineer role focuses on extending and scaling the agentic platform—making agents more capable, strengthening the API and tool layer, and expanding an ecosystem that external agents, clients, and partners can connect to.
You will work across the full path from a customer's agent to 6sense data and actions, including APIs, tools, MCP, orchestration, context, evaluation, and security. The role spans four major areas:
**Agents and Orchestration:** Extend LangGraph orchestrators with typed state, conditional routing, subgraphs, checkpointing, durable execution, and human-in-the-loop workflows. Implement routing, orchestrator-worker, plan-and-execute, reflection, and multi-agent handoff patterns. Run agents headless through API-first and MCP-first architectures across in-app, chat, workflow, and partner surfaces. Build reusable components for state management, memory, tool-calling, and agent-to-agent delegation with scoped permissions.
**APIs, Tools, and MCP:** Evolve APIs and tools agents use to access 6sense data and take action. Optimize tool design for agent success (naming, descriptions, schemas, response formats, error handling, token efficiency). Re-design and scale the MCP layer for production workloads including concurrency, per-tenant quotas, latency budgets, caching, and graceful degradation. Onboard external MCP clients, AI assistants, agent platforms, and partners. Strengthen platform reliability through idempotency, retries, rate limits, permissions, and spend controls.
**Context and Evaluation:** Partner with the Contextual Intelligence team to expose context through MCP, APIs, and tools. Define and enforce contracts around returned data, freshness, provenance, and access controls. Improve permission-aware retrieval and grounding through chunking, hybrid search, reranking, citations, and memory management. Expand evaluation frameworks using golden datasets, tool-selection accuracy, end-to-end task success metrics, and regression testing. Enhance tracing and usage analytics.
**Security, Privacy, and Trust:** Make security a first-class consideration across every tool, API, agent, and MCP change. Enforce tenant isolation and user-level authorization. Protect against prompt injection, tool poisoning, excessive agent autonomy, and data leakage. Establish runtime guardrails around steps, tokens, execution time, and spend.
You will also investigate and resolve customer-reported issues, participate in customer and partner technical discussions, write design documents, conduct code reviews, mentor engineers, and participate in on-call rotations. You'll stay current with developments in MCP, agent frameworks, and model capabilities.
**Requirements:**
- 5+ years of experience building and operating production software systems, with strong distributed systems fundamentals including idempotency, fault tolerance, backpressure, and failure recovery
- 2+ years of experience building production LLM-powered systems, including customer-facing agents
- Strong Python development skills and production experience with LangGraph or comparable orchestration framework
- Experience designing APIs and tool interfaces for LLM consumers, including schemas, descriptions, error handling, and tool selection optimization; direct MCP experience strongly preferred
- Experience scaling customer- or partner-facing platforms, including authentication, rate limiting, versioning, and backward compatibility
- Hands-on experience with retrieval, grounding, and LLM evaluation frameworks
- Experience building multi-tenant, security-sensitive cloud systems (AWS preferred), including authorization boundaries and data leakage prevention
- Proven ability to debug production issues across distributed services and support customers directly
- Demonstrated success leading multi-month, cross-functional initiatives with minimal oversight and strong communication skills
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
**Nice to Have:**
- Experience building or operating MCP servers in production or contributing to MCP open-source projects
- Experience serving context to agents from signals, entity data, knowledge graphs, or enterprise data platforms
- Experience integrating with external agent platforms, AI assistants, or agent interoperability protocols
- Familiarity with B2B go-to-market platforms, RevTech, CRM systems, or intent-data platforms
- Experience with model routing, caching, cost optimization, and latency optimization
- Experience with Kafka, Snowflake, Databricks, Spark, or similar data platforms
