Job Description

6sense has agents and an MCP server in production today, used by real customers. This role takes them further by building more capable agents, strengthening the API and tool layer, and creating an ecosystem that external agents, clients, and partners can connect to.

You'll work across the full journey from a customer's agent to 6sense data and actions—including APIs, tools, MCP, orchestration, context, evaluation, and security.

6sense is working to become the default Contextual Intelligence layer for B2B. Agents and MCP are how that context reaches customers, partners, and their AI tools. You'll partner closely with the team building contextual intelligence, write production Python daily, own multi-month projects, support the teams and customers who depend on these systems, and keep the platform current as the agent tooling landscape evolves.

What You'll Own

Agents and Orchestration

• Extend LangGraph orchestrators with typed state, conditional routing, subgraphs, checkpointing, durable execution, and human-in-the-loop steps.

• Choose among routing, orchestrator-worker, plan-and-execute, reflection, and multi-agent handoff patterns based on the problem.

• Run agents headless—API- and MCP-first—across in-app, chat, workflow, and partner surfaces from one shared core.

• Maintain reusable components for state, memory, tool-calling, and agent-to-agent delegation with scoped permissions.

APIs, Tools, and MCP

• Evolve the APIs and tools agents use to read 6sense data and take action, for both human and LLM callers.

• Tune tool design for agent success, including names, descriptions, schemas, response shapes, error messages, and token-efficient outputs.

• Redesign and scale the MCP layer for production workloads, including concurrency, per-tenant quotas, latency budgets, caching, and graceful degradation, while maintaining backward compatibility and adopting new specification capabilities as they mature.

• Onboard external MCP clients, AI assistants, agent platforms, and partners. Write integration guides and reference clients, and define how 6sense consumes third-party MCP servers, including trust boundaries.

• Harden execution with idempotency, retries, rate limits, permissions, and spend controls. Maintain standards for tool contracts, versioning, and discovery.

Context and Evaluation

• Partner with the Contextual Intelligence team to expose context to agents through MCP, APIs, and tools, ensuring one context layer serves every surface. Establish contracts defining what each call returns, how fresh the information is, where it comes from, and how it is scoped to the tenant and user.

• Improve permission-aware retrieval and grounding through chunking, hybrid search, reranking, and citations. Optimize context assembly and memory within token and latency limits.

• Extend evaluation using golden datasets, tool-selection and tool-call accuracy, end-to-end task success, regression suites, and release gates, including context-quality measures.

• Expand tracing and usage analytics, and report on task success rate, tool-call precision, groundedness, latency, and cost per successful outcome.

Security, Privacy, and Trust

• Make security a first consideration in every tool, API, agent, and MCP change, and review your own and teammates' work for vulnerabilities.

• Enforce tenant isolation and user-level authorization on every call so agents never access data beyond the caller's permissions. Maintain short-lived, scoped credentials.

• Defend against prompt injection, tool poisoning, excessive agency, and data leakage through tool outputs, traces, and logs. Set runtime limits on steps, tokens, time, and cost.

Customers and Engineering Craft

• Triage and debug customer-reported issues across agents, tools, models, and downstream systems, identify root causes, and deliver fixes.

• Participate in customer and partner technical calls, and support enablement teams with guidance, demos, and documentation.

• Write design documents that clearly explain assumptions and trade-offs, review code and designs, mentor engineers earlier in their careers, and participate in on-call rotations and incident response.

• Track developments in MCP, agent frameworks, and model capabilities. Prototype promising technologies and provide short, evidence-backed recommendations.

What You'll Bring

• 5+ years of experience building and running production software, with strong distributed-systems fundamentals, including idempotency, fault tolerance, backpressure, and failure recovery.

• 2+ years of experience building production LLM-based systems, including agents used by real customers.

• Strong Python skills and production experience with LangGraph or an equivalent orchestration framework.

• Experience designing APIs and tool interfaces for LLM callers, including schemas, descriptions, error handling, and tool-selection behavior. Direct MCP experience is strongly preferred.

• Experience scaling an API or platform used by external customers or partners, including authentication, rate limiting, versioning, and backward compatibility.

• Production experience with retrieval and grounding, as well as writing and running LLM evaluations.

• Experience with multi-tenant, security-sensitive cloud systems, preferably AWS, including authorization boundaries and data-leakage prevention.

• Experience debugging production issues across services and supporting customers directly or through support and solutions teams.

• A track record of owning multi-month, cross-team projects independently and communicating clearly with technical and non-technical audiences.

• 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 or AI assistants as a partner, or working with agent interoperability protocols.

• Experience with B2B go-to-market, RevTech, CRM, or intent-data platforms.

• Experience with model routing, caching, and cost/latency optimization.

• Experience with Kafka, Snowflake, Databricks, or Spark.

What This Role Is Not

• Not an architect role: You'll set direction through working code and design documents.

• Not MCP-only: MCP sits alongside APIs, tools, and orchestration.

• Not a greenfield build: These systems are live, with real customers depending on them.