Lead Forward Deployed Engineer
Lead Forward Deployed Engineer
About Us
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.
Role Overview
The Lead Forward Deployed Engineer (Lead FDE) owns delivery excellence across a portfolio of client accounts. The closest analogy is a coach: you are accountable for how the team performs in the field. You set the standard, guide technical direction across engagements, develop the engineers doing the work, make delivery adjustments in-flight, and ensure clients trust both the team and the outcome.
Where a Senior FDE goes deep on one hard problem, a Lead FDE holds the quality bar across several at once-without becoming the bottleneck. That means knowing when to coach someone through a problem and when to step in yourself, and having the technical credibility to do either. You will review critical architecture before it reaches clients, and you will give the engineers on your accounts the direct, specific feedback that actually makes them better.
This is a field leadership role. Your focus is delivery, technical direction, and developing the team on live client work-not administrative process.
Key Responsibilities
1. Own Portfolio Delivery Quality
Maintain a clear view of delivery status, risks, blockers, and priorities across assigned accounts.
Ensure each engagement has a practical execution plan, clear ownership, and an appropriate technical path.
Hold every account team to Eliza's quality bar.
Intervene early when delivery quality, pace, client confidence, or execution discipline is at risk.
Keep delivery aligned to client outcomes, engagement scope, and business priorities.
2. Set Technical Direction Across Engagements
Guide solution architecture and technical decision-making across multiple concurrent engagements.
Review critical work before it is shared with clients.
Ensure technical decisions are pragmatic, maintainable, and appropriate for each client's environment.
Help teams make clear tradeoffs between speed, quality, scope, and client value.
Identify when an engagement needs additional technical expertise or senior escalation.
3. Coach and Develop the Field Team
Coach the engineers on your accounts based on their real engagement work, not abstractions.
Provide direct, timely, actionable feedback on technical execution, client communication, ownership, and judgment.
Help team members improve how they frame tradeoffs, manage ambiguity, and drive work to completion.
Run milestone retrospectives so teams learn from each phase of an engagement and adjust.
Recognize when someone is ready for more responsibility, and when someone needs more support.
Keep the team focused, motivated, and aligned around client outcomes.
4. Maintain Client Confidence
Serve as the senior technical and delivery presence for clients across your portfolio.
Ensure client communication is clear, credible, and appropriately proactive.
Surface risks early and manage expectations with discipline.
Help clients understand the path from problem definition to shipped outcome.
Partner with account and commercial leadership so delivery realities are reflected in account strategy.
5. Establish Execution Discipline
Define the right operating cadence for each account: internal reviews, client checkpoints, technical reviews.
Ensure work is broken down clearly and assigned to accountable owners.
Track commitments, decisions, blockers, and client feedback.
Create enough structure for teams to move quickly without unnecessary process.
Keep teams focused on outcomes rather than activity.
6. Manage Delivery Risk and Escalation
Identify technical, staffing, scope, timeline, and client-alignment risks across the portfolio.
Decide when to coach the team through an issue versus when to intervene directly.
Escalate material risks to executive and account leadership with a clear recommendation.
Help the business distinguish between delivery issues, resourcing issues, scope issues, and client-alignment issues.
Qualifications
Required
8+ years of software engineering experience, with meaningful time in client-facing or field delivery roles.
Demonstrated ownership of delivery quality across multiple concurrent engagements or accounts.
Deep hands-on technical credibility: strong Python, production AI/ML delivery, and sound architectural judgment. You still read the code.
Proven experience developing engineers through direct, specific, timely feedback.
Excellent executive and technical communication-credible with a client CTO and with an engineer on the same day.
Deep comfort with modern cloud platforms (AWS, GCP, or Azure), CI/CD, and production deployment practices.
Strong commercial awareness: you understand how delivery decisions affect account health and business outcomes.
Preferred
Substantial production experience with LLMs (e.g., Anthropic, OpenAI, Cohere), vector search, retrieval-augmented generation, or agentic systems.
Prior consulting, professional services, or engagement leadership experience in a technical services environment.
Familiarity with MLOps practices and tooling (e.g., MLflow, Weights & Biases, SageMaker).
Working knowledge of enterprise security, data privacy, and compliance constraints.
Experience scaling delivery standards across a growing team without becoming the bottleneck.
What We Offer
Competitive compensation (salary + deployment bonuses or client uplift incentives).
Equity options in a growing AI services company.
Fully remote work
Remote work perks include a WFH stipend and monthly lifestyle stipend
Real ownership of a client portfolio and the team delivering it.
A leadership role that keeps you technical and in the field.
Flexibility to work across industries and problem domains.
A collaborative, mission-driven team passionate about the real-world impact of AI.