Eliza - Senior Forward Deployed Engineer
Requirements
• 5+ years of software engineering experience, including significant backend or full-stack depth. • Track record of independently owning complex, ambiguous technical work from definition through production. • Strong programming skills in Python; fluency in at least one additional language (JavaScript/TypeScript, Go, or similar). • Demonstrated experience delivering real-world ML/AI systems in production, not just prototypes. • Deep comfort with modern cloud platforms (AWS, GCP, or Azure), infrastructure-as-code, and CI/CD workflows. • Excellent client communication: you can hold a room, explain a tradeoff, and deliver bad news early. • Sound architectural judgment and the instinct to escalate at the right moment rather than the last moment. • Preferred • Substantial production experience with LLMs (e.g., Anthropic, OpenAI, Cohere), vector search, retrieval-augmented generation, or agentic systems. • Prior consulting, professional services, solutions architecture, or forward-deployed engineering experience. • Familiarity with MLOps practices and tooling (e.g., MLflow, Weights & Biases, SageMaker). • Working knowledge of enterprise security, data privacy, and compliance constraints. • Experience mentoring engineers through influence rather than authority.
Responsibilities
• 1. Lead Complex Technical Delivery • Own end-to-end delivery quality for a major engagement, workstream, or solution area. • Translate ambiguous client needs into practical execution plans, technical milestones, and shipped outcomes. • Maintain a clear view of status, risks, blockers, and dependencies within your scope. • Raise quality concerns early and correct course before client confidence is affected. • 2. Own Architecture and Technical Tradeoffs • Lead solution architecture and technical decision-making for your workstream. • Make pragmatic tradeoffs between speed, quality, scope, maintainability, cost, latency, and client value—and explain your reasoning. • Review critical technical work before it is shared with clients or treated as production-ready. • Recognize when a problem needs deeper expertise or a second set of eyes. • Turn hard-won solutions into reusable patterns that raise the floor for future engagements. • 3. Build and Deploy Production AI Systems • Design and ship AI/ML solutions using Python, modern ML frameworks, LLM orchestration tooling, and cloud-native infrastructure. • Integrate LLMs and other generative models into client products and workflows at production quality. • Own fine-tuning, prompt engineering, retrieval, and evaluation pipelines where the problem calls for them. • Make sound decisions about security, data privacy, and compliance constraints in enterprise environments. • 4. Earn and Maintain Client Trust • Act as the senior technical voice on your engagement, credible with both engineers and executives. • Communicate direction, tradeoffs, risks, and progress clearly to technical and non-technical audiences. • Manage expectations with discipline when scope, timeline, data, or technical constraints change. • Keep account and commercial partners informed so client strategy reflects delivery reality. • 5. Handle Risk with Judgment • Spot technical, delivery, scope, timeline, and client-alignment risks early. • Resolve what you can directly; escalate what you cannot, with clear context, options, and a recommendation. • Intervene decisively within your scope when quality, pace, or client confidence is at risk. • 6. Lift the Engineers Around You • Mentor less experienced FDEs through technical review, pairing, and practical feedback. • Help teammates make better architecture, implementation, and communication decisions. • Model what excellent forward-deployed execution looks like in client-facing work. • Contribute to interview loops, onboarding, and technical standards.
Benefits
• Competitive compensation (salary + annual bonus). • Equity options in a growing AI services company. • Fully remote work • Remote work perks include a WFH stipend and monthly lifestyle stipend • A genuine senior IC track—technical leadership with real scope, without a forced move into people management. • The hardest problems in the portfolio, and the autonomy to solve them. • Flexibility to work across industries and problem domains. • A collaborative, mission-driven team passionate about the real-world impact of AI.
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