Sr. AI Engineer

Overview

Senior AI Engineer to extend 6sense's live agents and MCP server, building more capable agents, strengthening the API and tool layer, and creating an ecosystem external agents, clients and partners can connect to.

What you'll do

• 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.
• 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.
• Onboard external MCP clients, AI assistants, agent platforms and partners; write integration guides and reference clients; 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.
• Partner with the Contextual Intelligence team to expose context to agents through MCP, APIs and tools, defining contracts for return values, freshness, source and tenant/user scoping.
• 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.
• Make security a first consideration in every tool, API, agent and MCP change; enforce tenant isolation and user-level authorization on every call; 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.
• Triage and debug customer-reported issues across agents, tools, models and downstream systems, and deliver fixes.
• Participate in customer and partner technical calls; support enablement teams with guidance, demos and documentation.
• Write design documents explaining assumptions and trade-offs; review code and designs; mentor earlier-career engineers; 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 need

• 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.
• 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

• Direct MCP experience (strongly preferred).
• 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.

Details

• Location: Bengaluru, Karnataka, India.
• Full-time employment.