Sr. AI Engineer

Responsibilities

You will build and scale agentic systems, APIs, and MCP layers to enhance contextual intelligence for B2B customers. This involves managing the full lifecycle of agent orchestration, ensuring security, and maintaining production-grade performance.

Requirements

The role requires 5+ years of software engineering experience with strong distributed systems fundamentals and 2+ years of production experience with LLM-based systems. Proficiency in Python and experience with orchestration frameworks like LangGraph are essential.

Skills

• Python
• LangGraph
• LLM
• API design
• MCP
• Distributed systems
• Orchestration
• Retrieval-augmented generation
• AWS
• Data security
• Prompt engineering
• System architecture
• Debugging
• Technical documentation
• Mentoring
• Multi-tenancy

Benefits

• Health coverage
• Paid parental leave
• Paid time-off
• Holidays
• Quarterly self-care days
• Stock options
• Wellness education sessions

About this role

Our Mission:
6sense's mission is to multiply what matters: growth, retention, and efficiency. We envision a future where companies, teams and people reach their full potential.
Our People:
People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging. Every 6sensor plays a part in defining the future of our industry-leading technology. 6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers. We want 6sense to be the best chapter of your career.
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 debuggin...

About 6sense

Software Development · 1,001-5,000 employees · headquartered in San Francisco, California · founded 2013