Eliza is a technology services company and Advanced-tier OpenAI partner that’s dedicated to helping organizations build and deploy cutting-edge AI solutions. From generative AI and custom LLM integrations to predictive analytics and intelligent automation, we work across industries to bring real-world AI applications to life. Our projects combine deep technical expertise with hands-on client collaboration to solve high-impact problems.
The Senior Forward Deployed Engineer (Senior FDE) executes the core technical work on our hardest client problems. This is a senior individual contributor role with real depth: you take ambiguous, high-stakes work from problem definition through shipped outcome, own the architecture and the tradeoffs, and set the technical standard clients experience.
Senior FDEs go deep on a major engagement or workstream rather than broad across a portfolio. You are the person we send when the problem is genuinely difficult, the requirements are unclear, and the client needs someone who can hold the technical line while still shipping. You will review critical work before it reaches clients and lift the engineers around you through pairing, review, and example.
This role is for engineers who want the hardest technical problems and direct client exposure without trading hands-on work for a management track.
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.
Required
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.
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.
Build and maintain automated security assurance, observability, and compliance systems, integrating practical security controls into software development and operational workflows across cloud-native services.
Build and maintain automated security assurance, observability, and compliance systems, integrating practical security controls into software development and operational workflows across cloud-native services.
Own architecture and deliver production-grade data and AI solutions for enterprise customers, combining data engineering, machine learning, and application development while guiding technical decisions and customer adoption.
Build automated security assurance, observability, and compliance systems for cloud-native services, integrating practical controls into software development and operational workflows across the organization.
Build and maintain automated security assurance, observability, and compliance systems, integrating practical security controls into software development and operational workflows across cloud-native services.
