Senior AI Engineer

6sense; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Health & Wellness: Parental Leave, etc

Flexible Time Off

WeWork Access

WFH Stipend

Management Training

Job summary

6sense is building the default Contextual Intelligence layer for B2B. This role focuses on expanding agent capabilities, strengthening the MCP-driven API/tool surface, and enabling external agents and partners to connect to the platform.

You will work across the full stack from agents to data and actions, collaborating with contextual intelligence teams and ensuring secure, scalable workflows in a production environment.

Qualifications

• Experience designing APIs and tool interfaces for LLM consumers, including schemas, descriptions, error handling, and tool selection optimization.

Responsibilities

• Build more capable agents, strengthen the API and tool layer, and expand an ecosystem for external agents, clients, and partners to connect.

Skills

Python development

Production LLM-powered systems

LangGraph

Distributed systems

Security-conscious design

Cross-functional communication

On-call rotations

MCP experience

Education

Bachelor’s degree in Computer Science or related

Tools

LangGraph

AWS

Kafka

Snowflake

Databricks

Spark

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 expanding an ecosystem that external agents, clients, and partners can connect to
• You’ll work across the full path 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, and 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
• Extend LangGraph orchestrators with typed state, conditional routing, subgraphs, checkpointing, durable execution, and human-in-the-loop workflows
• Select and implement routing, orchestrator-worker, plan-and-execute, reflection, and multi-agent handoff patterns based on business needs
• Run agents headless through API-first and MCP-first architectures across in-app, chat, workflow, and partner surfaces from a shared core
• Build and maintain reusable components for state management, memory, tool-calling, and agent-to-agent delegation with scoped permissions
• Evolve the APIs and tools agents use to access 6sense data and take action for both human and LLM consumers
• Optimize tool design for agent success, including naming, descriptions, schemas, response formats, error handling, and token efficiency
• Re-design and scale the MCP layer for production workloads, including concurrency, per-tenant quotas, latency budgets, caching, graceful degradation, backward compatibility, and adoption of emerging MCP capabilities
• Onboard external MCP clients, AI assistants, agent platforms, and partners. Develop integration guides and reference clients and define trust boundaries for third-party MCP server integrations
• Strengthen platform reliability through idempotency, retries, rate limits, permissions, spend controls, and standards for tool contracts, versioning, and discovery
• Partner with the Contextual Intelligence team to expose context through MCP, APIs, and tools, enabling a unified context layer across all customer touchpoints
• Define and enforce contracts around returned data, freshness, provenance, and tenant and user-level access controls
• Improve permission‑aware retrieval and grounding through chunking, hybrid search, reranking, citations, context assembly, and memory management within token and latency constraints
• Expand evaluation frameworks using golden datasets, tool‑selection accuracy, tool‑call accuracy, end‑to‑end task success metrics, regression testing, release gates, and context quality measurements
• Enhance tracing and usage analytics while reporting on task success rates, tool‑call precision, groundedness, latency, and cost efficiency
• Make security a first‑class consideration across every tool, API, agent, and MCP change
• Review your own and teammates’ work for vulnerabilities and security risks
• Enforce tenant isolation and user‑level authorization for all requests while maintaining short‑lived, scoped credentials
• Protect against prompt injection, tool poisoning, excessive agent autonomy, and data leakage through outputs, traces, and logs
• Establish runtime guardrails around steps, tokens, execution time, and spend
• Investigate and resolve customer‑reported issues across agents, tools, models, and downstream systems
• Participate in customer and partner technical discussions and support enablement teams through guidance, demonstrations, and documentation
• Write design documents, evaluate trade‑offs, conduct code reviews, mentor engineers, and participate in on‑call and incident response rotations
• Stay current with developments in MCP, agent frameworks, and model capabilities, prototyping promising technologies and sharing evidence‑based recommendations

Benefits

• Health & Wellness: Parental Leave, Wellness Days Off, Quarterly Wellness Speakers, Wellness Challenges
• Giving Back: Giving Fridays Rotating Charity Support, 6sense Gives Back Programs, Month of Service
• Social Well Being: Flexible Time Off, HUB Working Model, WeWork Access
• Professional Development: LinkedIn Learning Courses, WFH Stipend, Revenue Certifications, Management Training

• Experience designing APIs and tool interfaces for LLM consumers, including schemas, descriptions, error handling, and tool selection optimization.
• Direct MCP experience is strongly preferredExperience building multi-tenant, security-sensitive cloud systems (AWS preferred), including authorization boundaries and data leakage prevention
• Demonstrated success leading multi-month, cross-functional initiatives with minimal oversight and strong communication skills across technical and non-technical audiences
• Strong Python development skills and production experience with LangGraph or a comparable orchestration framework
• Proven ability to debug production issues across distributed services and support customers directly or through support and solutions teams
• Hands‑on experience with retrieval, grounding, and LLM evaluation frameworks
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
• 2+ years of experience building production LLM‑powered systems, including customer‑facing agents
• Experience scaling customer‑or partner‑facing platforms, including authentication, rate limiting, versioning, and backward compatibility
• 5+ years of experience building and operating production software systems, with strong distributed systems fundamentals including idempotency, fault tolerance, backpressure, and failure recovery
• 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