Forward Deployed Engineer, Fractal

• Technical depth (you’re an engineer first)
• 3–10 years of professional software engineering experience building and operating production systems
• Strong coding skills in Python plus experience with TypeScript/JavaScript or another modern language; comfortable working full-stack when needed
• Hands-on experience with at least one major cloud provider (AWS, GCP, or Azure), and with containers/orchestration (Docker, Kubernetes or equivalent)
• Practical experience with modern AI/LLM tooling: building agentic workflows, RAG pipelines, or integrating LLMs into real products
• Comfortable reading other people’s codebases quickly and making safe changes in unfamiliar environments
• Proven track record working directly with customers or stakeholders to deliver complex technical projects (consulting, implementation, solutions/field engineering, or similar)
• Excellent communication skills – you can explain technical constraints and risks to a VP in plain language without oversimplifying
• High ownership and collaboration mindset even while owning your informed opinions
• Bias to action: you’re comfortable moving from rough requirements to working prototypes quickly, then iterating in front of real users
• Ability to operate in ambiguous, changing environments – switching between customers, industries, and tech stacks without losing momentum
• Willingness to travel periodically to embed on-site with key customers when it materially accelerates deployment success

Desirable

• As a Forward Deployed Engineer, you are the engineer on the front line of customer value
• You’ll embed directly with strategic customers, understand their real business problems, and turn our platform and products into production systems that move the needle on customer outcomes
• You’ll spend most of your time writing and shipping production code in customer environments, integrating systems, building AI/ML-powered workflows, and deploying and supporting production deployments end-to-end until they’re stable, adopted, and delivering measurable outcomes
• This is an engineering-first role with deep customer exposure: you’ll work closely with senior stakeholders from business and technical teams on the customer side, travel on-site when it makes sense, and act as their trusted technical partner. Success is measured in value delivered, user adoption, and accumulation of platform and product leverage
• Own the technical success of deployments for a portfolio of strategic customers – from discovery and design through go-live and scale-out
• Translate ambiguous customer goals (“reduce manual review time”, “improve CS efficiency”, “cut claim processing costs”) into concrete technical roadmaps and implementation plans leveraging and extending our platform and products
• Define and track deployment KPIs (latency, accuracy, throughput, adoption, business impact) and continuously tune systems to hit them
• Design and implement full-stack solutions: backend services, data pipelines, integrations, and customer-facing assistants and workflows
• Build and harden production AI systems – including LLM- and agent-based workflows, knowledge pipelines, and domain-specific orchestration
• Integrate with customer infrastructure (APIs, data warehouses, identity, CRM/ERP, internal services) across one or more major clouds
• Own deployment lifecycles: proofs-of-value, pilots, phased rollouts, and post-go-live stabilization
• Debug gnarly production issues in unfamiliar environments, often under tight time pressure, and drive them to root cause and fix
• Establish monitoring, observability, and runbooks so customers can operate solutions with confidence
• Work side-by-side with customer engineers – pairing on code, reviewing designs, and guiding architecture decisions
• Communicate trade-offs clearly to non-technical stakeholders and help them make informed decisions without drowning them in jargon
• Influence customer teams to adopt best practices for AI, data, and software delivery – even when it means challenging their current approach
• Bring structured feedback from the field back into our product and engineering roadmap: common integration patterns, missing features, recurring edge cases, and opportunities to productize bespoke work
• Help shape our forward deployed playbook: reusable templates, reference architectures, example repos, and internal tooling that make future deployments faster and more repeatable

• End Date: January 19, 2026