Senior Software Engineer, AI Agentic Platform

Description

The company builds AI that makes the real world respond to people, starting with parking, where cars drive in and out without friction, and expanding into retail and hospitality.

The team is looking for builders and problem solvers to help build this next phase.

Responsibilities

• Design and build production-grade agentic platform infrastructure: the harness, orchestration pipelines, tool-use abstractions, MCP integrations, and context and memory management.
• Architect low-latency, high-throughput LLM inference routing and caching, plus semantic search interfaces that keep cost, performance, and token usage under control.
• Set up evaluation, tracing, and observability (LLMOps and AIOps) to measure agent accuracy, follow execution paths, and debug non-deterministic workflows.
• Build developer tooling, agent SDKs, and CI/CD automation so product engineering teams can ship autonomous AI agents safely and quickly.
• Implement enterprise-grade guardrails, rate limiting, sandboxing, and security policies for model execution and tool access.
• Make key architecture decisions on service boundaries, data consistency, multi-agent communication protocols, and overall reliability.
• Set technical standards, evaluate new AI tooling, and mentor peers on agentic architecture patterns.

Requirements

• You will architect and build the agent swarm, orchestration pipelines, and developer tooling for a Senior Software Engineer role. The work covers hard distributed systems problems at scale, build-versus-buy decisions on fast-changing AI infrastructure, and low-latency agent orchestration frameworks that let product teams deploy autonomous workflows for both SDLC and business process automation.
• You will work with a team that cares deeply about performance tuning, model routing, reliability engineering, and agent protocols, and collaborate across Application Engineering, Infrastructure, and Advanced Technologies.
• You have 6+ years of backend engineering experience, with a deep focus on distributed systems, platform infrastructure, and high-load production systems.
• You have 2+ years of hands-on experience architecting and deploying agentic AI systems, LLM orchestration frameworks, or AI platform tooling.
• You are strong in Python, or in Java or TypeScript, with production experience in async concurrency, API design, and system performance tuning.
• You have shipped production work with agentic orchestration frameworks and protocols such as LangGraph, Anthropic MCP, Google ADK, AutoGen, A2A, or LlamaIndex.
• You have expertise in microservices, Kubernetes, and AWS.
• You have observability and LLMOps experience, covering metrics, distributed tracing, structured logging, and debugging agent runs.
• You know relational databases, vector databases, event streaming (Kafka, SQS), and caching patterns.
• You have used AI-powered coding tools such as Claude Code, GitHub Copilot, or similar tools to boost productivity and manage context.
• You communicate technical ideas well, can design and document complex AI architectures, and work across cross-functional teams.
• Nice to have: an advanced degree in Computer Science, Machine Learning, or a related technical field.
• Nice to have: experience building custom agent harnesses, fine-tuning models, or running RLHF or RLAIF optimization loops.
• Nice to have: security compliance experience in data privacy, prompt injection defenses, secrets management, and access controls for financial or mobility data.
• Nice to have: open-source contributions to agent frameworks or MCP servers, or technical writing on agentic system architecture.
• Stack: TypeScript, React, Scala (mainly), and Java (limited). Datastores are MySQL, PostgreSQL, and Snowflake. Cloud is AWS. Version control uses Git and GitHub. AI tooling includes Copilot on GitHub and Claude Code. Observability runs on Datadog.

Conditions

The role is office-first: you must be on-site at least four days a week.

The anticipated base salary is $177,000.00 USD to $216,000.00 USD per year. The final offer depends on factors such as qualifications, years of relevant experience, distinctive skills, education, certifications or licenses, and where you live or work.

The company may use an automated employment decision tool (AEDT) to assess your application against the listed qualifications and responsibilities. It keeps relevant data, including personal information, for as long as the tool reasonably needs it, and if you are hired that data may join your employee record.

The company welcomes applicants regardless of background and aims for an inclusive culture.

Benefits

• Total compensation includes base salary plus healthcare eligibility, a 401(k) plan, short-term and long-term disability coverage, basic life insurance, a stock option plan, bonus plans, and more.