Join the crew

About the role

You'll deliver in an AI-native model where coding agents, LLM-powered components, and intelligent automation are core to how solutions are built and operated. You'll embed with customers, rapidly translate complex requirements into working systems, and act as the technical delivery lead throughout the engagement, moving fast with agents like Claude Code and Codex while holding to enterprise-grade quality.

What you'll do

AI-native solution delivery

• Design and build software where AI agents, LLMs, and automation are first-class architectural components.
• Lead end-to-end delivery in customer environments: architecture, development, deployment, and handover.
• Work across the full stack: backend services, data pipelines, APIs, integrations, and AI components.

Coding agent-driven development

• Use coding agents (Claude Code, Codex, Cursor, Copilot) as primary acceleration tools, with structured prompting and context management.
• Review, validate, and refactor agent-generated code with full rigor, catching hallucinations, security issues, and logic errors.
• Establish review gates and standards tuned to the failure modes of AI-generated code.

Agent design & orchestration

• Design multi-agent systems: roles, tool access, orchestration, handoffs, state, and error recovery.
• Build reliable pipelines (LangChain, LlamaIndex, AutoGen, or custom) with observability, audit logging, and human-in-the-loop checkpoints.

LLM integration & RAG

• Integrate LLM APIs into production with solid error handling and fallback logic.
• Build RAG pipelines (ingestion, chunking, embeddings, vector DBs, retrieval) with guardrails and output validation.

Enterprise quality & safety

• Test AI systems rigorously, accounting for non-deterministic outputs; design for graceful degradation and audit trails.
• Meet enterprise standards for security, data privacy, access control, and compliance; document behavior and known limits for safe handover.

What we're looking for

• 5+ years of software engineering, including recent hands-on work building and shipping AI-native applications.
• Strong in at least one of Python, Java, Go, or TypeScript; solid system design, data modeling, and cloud architecture.
• Production experience with LLM APIs, agent systems (tool use, function calling, orchestration), and RAG (embeddings, vector DBs).
• Daily hands-on use of coding agents; understanding of AI failure modes (hallucination, prompt injection) and how to guardrail them.
• Strong communicator; comfortable in ambiguous, customer-facing environments; willing to travel.

Nice to have

• Fine-tuning or evaluating LLMs; familiarity with enterprise systems (ERP, CRM, data warehouses) and modern data stacks.
• DevOps/infra (CI/CD, Kubernetes, Terraform); consulting or startup background; open-source AI/agent contributions.

About the role

You'll embed with customers, translate complex business and technical requirements into well-architected systems, and lead delivery through the whole engagement. You use coding agents like Claude Code to raise development velocity while holding a high bar for code quality, performance, and security, and you know when AI-assisted development helps and when a human engineering decision has to drive. This role suits experienced engineers who design scalable, secure, maintainable systems, do well in customer-driven environments, and want their work running real operations. Travel runs up to 40%.

What you'll do

Architecture & delivery

• Design end-to-end systems where scalability, reliability, security, and performance are first-order concerns.
• Lead technical delivery from architecture through development, testing, deployment, and operational handover.
• Work across the full stack: backend services, databases, APIs, integrations, cloud infrastructure, and DevOps.
• Embed with customer teams to understand their technical environment, business drivers, and constraints.

Agent-accelerated development

• Use coding agents (Claude Code, Cursor, GitHub Copilot) as primary tools to increase velocity, with structured prompting and task decomposition.
• Review, validate, and refactor all agent-generated code with full rigor, catching logic errors, performance bottlenecks, and security holes.
• Hold coding standards, performance baselines, and security gates across the codebase, and override the AI when human judgment should win.

Performance & reliability

• Optimize for latency, throughput, and resource efficiency with caching, async patterns, query optimization, and load distribution.
• Build in fault tolerance: graceful degradation, circuit breakers, retry logic, and failover.
• Instrument systems with logging, metrics, distributed tracing, and alerting; load-test and capacity-plan against real conditions.

Security & compliance

• Design authentication, authorization, encryption, and audit logging across systems.
• Apply secure coding practices against injection, privilege escalation, and data exposure; threat-model and run security reviews.
• Meet enterprise compliance requirements (SOC 2, ISO 27001, HIPAA, GDPR) and stay current with new threats.

Data & integration

• Design databases, data pipelines, and event-streaming architectures aligned to business requirements.
• Build reliable, auditable integrations with enterprise systems (ERP, CRM, data warehouses).
• Design APIs (REST, GraphQL, async) that clients can depend on and extend.

What we're looking for

• 7+ years of software engineering, including recent hands-on development of production enterprise systems.
• A track record shipping well-architected, reliable, secure systems under real load.
• Expert-level proficiency in at least one of Python, Java, Go, TypeScript, or C++.
• Deep experience with APIs, microservices, distributed and event-driven systems, and cloud architecture (AWS, GCP, Azure).
• Proficiency with relational (PostgreSQL, MySQL, Oracle) and NoSQL (MongoDB, Cassandra, DynamoDB) databases.
• CI/CD, containerization (Docker, Kubernetes), and infrastructure-as-code.
• Daily hands-on use of coding agents, with the discipline to review and validate AI-generated code.
• Strong communicator across technical and non-technical stakeholders; comfortable in ambiguous, customer-facing work; willing to travel.

Nice to have

• Enterprise systems (ERP, CRM, data warehouses) and complex integration patterns; modern data stacks (data lakes, semantic layers, streaming).
• Financial or fintech systems, or other highly regulated environments; open-source infrastructure or backend contributions.
• Consulting, solutions engineering, or startup background; published work or talks on system design and software engineering.

About the role

You'll embed with customer organizations, lead discovery workshops, map complex processes, and rapidly prototype AI-powered solutions to validate ideas before full engineering investment. You bring domain depth and consulting instincts, plus genuine hands-on fluency with AI tools: using coding agents and LLM APIs to compress the distance between a customer's problem and a working proof of concept. You must understand AI-native systems well enough to define them, prototype them, and hand them off credibly to engineering.

What you'll do

Discovery & business analysis

• Deeply understand each customer's industry, operating model, and objectives through structured discovery and stakeholder interviews.
• Translate ambiguous challenges into clear, scoped problem statements; document as-is and desired future state with measurable success criteria.

AI-native solution design

• Design end-to-end concepts where agents, LLMs, and automation are first-class components, defining roles, orchestration, and human-in-the-loop touchpoints.
• Judge where AI should replace, augment, or accelerate workflows, and where traditional software is the right tool.
• Partner with FDE Software Engineers to keep designs feasible, safe, and aligned to platform capabilities.

AI-enabled prototyping

• Build working prototypes and POC demos with coding agents (Claude Code, Codex, Cursor) and LLM APIs.
• Present prototypes to stakeholders to gather feedback, build alignment, and de-risk investment; know when to hand off to engineering.

Customer engagement & advisory

• Serve as the primary customer-facing advisor; lead executive briefings and AI readiness workshops.
• Guide customers on AI adoption: change management, data readiness, governance, and human-AI workflow design.

What we're looking for

• 7+ years in management consulting, solutions engineering, business analysis, or domain operations, leading complex customer engagements.
• Deep understanding of at least one enterprise domain (finance, HR, supply chain, sales ops, customer service) and business process modeling.
• Hands-on use of LLM APIs and coding agents to build working prototypes yourself.
• Ability to design concepts involving agents, orchestration, and RAG well enough to define requirements and judge feasibility.
• Exceptional communication and facilitation, from frontline operators to the C-suite; willing to travel.

Nice to have

• Big 4 / boutique strategy / enterprise implementation background; scripting (Python, JavaScript) for richer prototyping.
• Familiarity with agent frameworks; product management, UX research, design thinking, or agile/lean; platform certifications (SAP, Salesforce, Workday).

About the role

This role sits at the intersection of LLM research and production software engineering. You'll work across model post-training, evaluation, coding agents, synthetic data, secure execution environments, and production infrastructure, moving AI from impressive demos to reliable enterprise systems. Working closely with our founders and technical leadership, you'll experiment, build, and ship AI capabilities that go straight into customer deployments.

What you'll do

• Evaluate frontier and open-weight models to find the best fit for customer workloads and deployment constraints.
• Design evaluation frameworks and benchmarks for coding agents and enterprise AI workflows.
• Build and improve post-training pipelines.
• Develop synthetic-data pipelines that raise model quality for enterprise use cases.
• Optimize models for production through quantization, inference optimization, and performance tuning.
• Turn research and experimentation into reliable production systems that keep improving through real-world usage and customer feedback.

What we're looking for

• You enjoy working across the whole applied AI stack, from training models to shipping the infrastructure that runs them.
• BS or MS in Computer Science or a related technical field, or equivalent experience.
• About 3–5 years of professional experience, including strong Python backend development.
• Hands-on experience building LLM, generative AI, or applied machine learning systems.
• Familiarity with modern post-training techniques such as SFT, RLHF, DPO, GRPO, or instruction tuning.
• Strong software engineering fundamentals and the ability to ship production-quality systems that hold up in real use.
• Experience designing experiments, evaluating model performance, and making data-driven technical decisions.
• Comfort using frontier models and AI coding tools in your daily development workflow.
• Curious and collaborative, at home across research, engineering, and customer-facing technical work in a fast-moving startup.

Bonus points

• Experience building coding agents or agentic AI systems.
• Model quantization or inference optimization experience.
• Contributions to open-source AI infrastructure, evaluation frameworks, or benchmark suites.
• Experience deploying AI in enterprise environments with security, governance, auditability, or compliance requirements.
• Publications, technical blogs, or research on LLMs, post-training, evaluation, or agentic systems.