Staff Software Engineer

Role Description

As a Forward Deployed Engineer, you will operate at the front lines of innovation — embedded directly with subject matter experts, product, and engineering teams to rapidly design, architect, prototype, and deploy solutions that solve high-priority business problems. This is not a traditional engineering role with long delivery cycles. You will be expected to go from concept to working prototype in days, not months, while maintaining enterprise-grade standards for quality, security, and scalability.

You bring an exceptional combination of skills:

• Deep hands-on engineering experience across AI agents and full-stack development.
• Interpersonal skills to earn trust, drive alignment, and influence without authority.
• Comfortable whiteboarding a system architecture with senior stakeholders and writing production code the same afternoon.
• Thrive in fast, ambiguous environments where creative thinking and execution speed matter.
• Dependable partner when production systems need attention.

Key Responsibilities

• Solution Design & Architecture
  • Lead solution design for complex, cross-functional data and AI problems — from initial discovery through to technical blueprint.
  • Define and communicate architecture decisions, trade-offs, and delivery approaches to both technical and non-technical audiences.
  • Design scalable, modular systems that balance the need for speed with enterprise standards for reliability, security, and maintainability.
  • Participate in architecture reviews, ensuring alignment with enterprise patterns and platform standards.
  • Create clear technical documentation: architecture diagrams, data flow maps, API contracts, and solution briefs.
• Rapid Prototyping & Solution Delivery
  • Design and deliver working prototypes for complex data and AI problems within compressed timeframes, often days to weeks.
  • Translate ambiguous business requirements into concrete technical solutions with minimal hand-holding.
  • Balance speed of delivery with enterprise standards — your prototypes are production-ready, not throwaway.
  • Continuously iterate on solutions based on direct feedback from product managers, program leads, and end users.
  • Develop intuitive front-end interfaces and dashboards that bring data and AI outputs to life for business users.
• AI Agent Development
  • Design, build, and deploy AI agents and multi-agent systems that automate complex workflows end-to-end.
  • Develop and maintain agent skills — discrete, reusable capabilities that compose into larger agentic pipelines.
  • Implement and extend Model Context Protocol (MCP) servers and clients to connect AI agents with enterprise tools, APIs, and data sources.
  • Design evaluation harnesses, guardrails, and monitoring pipelines to ensure agent reliability and safety in production.
  • Stay current with the rapidly evolving agentic AI landscape and proactively introduce new techniques and tooling to the team.
  • Integrate LLMs, RAG systems, and ML models into production workflows.
• Application Support & Operational Reliability
  • Serve as a technical point of contact for production issues across deployed AI and data applications, triaging and resolving incidents promptly.
  • Monitor application health, respond to alerts, and coordinate with platform and infrastructure teams to maintain SLA commitments.
  • Contribute to post-incident reviews, identify root causes, and implement preventive measures to reduce recurrence.
  • Maintain runbooks and operational documentation so that support responsibilities are scalable and transferable across the team.
• Collaboration & Stakeholder Engagement
  • Embed directly with product, program, and engineering teams to co-define problems and co-deliver solutions.
  • Influence technical direction and build alignment across teams without relying on formal authority.
  • Communicate complex technical concepts clearly to non-technical business stakeholders — in writing, in meetings, and in executive presentations.
  • Mentor and elevate junior engineers, sharing patterns and practices for agentic development, prompt design, and rapid delivery.
  • Foster a collaborative, low-ego team culture where speed and quality go hand in hand.

Qualifications

• Years of professional software engineering experience proportional to level, including solution design and architecture ownership.
• Demonstrated ability to architect end-to-end systems — from requirements through deployment — with clear documentation and stakeholder communication.
• Hands-on experience building AI agents, including defining agent skills, tool use, memory, and multi-step reasoning.
• Experience with AI-Augmented Engineering — actively using tools like Claude Code, GitHub Copilot, or equivalent assistants to accelerate coding, documentation, and problem-solving day-to-day.
• Direct experience with AWS Agent Core, Azure AI Foundry, Google Vertex AI Agents, or equivalent production agent infrastructure — building, deploying, and operating agents in production.
• Working knowledge of Model Context Protocol (MCP) — including building or consuming MCP servers to connect agents with external systems.
• Experience with agentic frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or Semantic Kernel.
• Proficiency in Python and at least one front-end framework (React, Vue.js, or Angular).
• Experience with cloud platforms (AWS, Azure, or GCP).
• Exceptional communication and interpersonal skills — you can earn trust quickly, navigate ambiguity, and drive alignment across diverse teams.
• Comfort operating in fast-paced environments with shifting priorities, high ownership expectations, and occasional on-call responsibility.
• Experience mentoring or supporting junior engineers in their technical development.

Desirable Skills

• Experience with RAG architectures, vector databases (Pinecone, Weaviate, pgvector), and semantic search.
• Familiarity with prompt engineering, fine-tuning, and LLM evaluation techniques.
• Experience with agent observability and tracing tools (LangSmith, Arize, Weights & Biases, or similar).
• Strong UX instincts — ability to simplify complex flows and make agent outputs accessible and actionable for non-technical stakeholders.
• Experience with containerization and CI/CD practices (Docker, Kubernetes, GitHub Actions).
• Background in real estate, financial services, or other data-intensive enterprise domains.
• Experience facilitating technical discovery workshops, design sprints, or architecture reviews.

Benefits

• 401(k) plan with matching company contributions.
• Comprehensive Medical, Dental & Vision Care.
• Paid parental leave at 100% of salary.
• Paid Time Off and Company Holidays.
• Early access to earned wages through Daily Pay.