Lead Forward Deployed Engineer

About the role

Eliza is a leading partner of OpenAI, dedicated to creating and implementing innovative AI solutions for a wide range of clients. In this pivotal position, you will act as a field coach, responsible for overseeing the technical delivery and performance of teams across various client accounts. Your role will involve upholding engineering excellence while mentoring engineers and guiding architectural decisions for the projects you manage. You will translate strategic objectives into actionable plans that ensure robust and scalable technical outcomes. This position demands a proactive mindset that anticipates risks and opportunities within the project lifecycle. You will serve as the critical link between client expectations and the technical execution required to meet them. Success in this role requires a balance of hands-on coding expertise and high-level leadership. You will foster a culture of continuous improvement and learning within the engineering teams you oversee.

Key facts

What you'll do

Coordinate with client stakeholders to define project scope and technical requirements, ensuring alignment with business goals.

Architect and implement production-level AI and machine learning solutions using Python, focusing on reliability and scalability.

Deploy and manage infrastructure on major cloud platforms such as AWS, GCP, and Azure to support application performance.

Establish and enforce CI/CD pipelines to automate testing and deployment, reducing manual errors and accelerating delivery.

Conduct in-depth code reviews to maintain code quality and share best practices across the engineering organization.

Mentor engineers on advanced concepts related to large language models and vector search technologies to elevate team capabilities.

Monitor the health and performance of deployed systems, identifying bottlenecks and driving optimizations proactively.

Utilize MLOps tools like MLflow, Weights & Biases, and SageMaker to track experiments and streamline model lifecycle management.

Evaluate and integrate retrieval-augmented generation and agentic system patterns to enhance solution intelligence.

Analyze client feedback and system metrics to iteratively refine the delivery approach and improve satisfaction.

Champion security and compliance standards throughout the development process to protect data integrity.

Facilitate knowledge transfer sessions to ensure continuity and consistency across project teams.

Adapt delivery strategies based on the specific needs of enterprise clients and the constraints of their environments.

Drive the documentation of architectures and processes to ensure clarity and maintainability for future work.

Requirements

Possess a minimum of 8 years of experience in software engineering, with significant time spent in roles focused on field delivery or client engagement.

Demonstrate a proven track record of managing the quality of delivery across multiple concurrent client accounts without compromising standards.

Exhibit strong technical expertise in Python, particularly in the context of production-level AI and machine learning implementations.

Show a history of providing targeted coaching and mentorship to engineers, helping them enhance their technical skills and project contributions.

Maintain excellent communication skills, capable of conveying complex technical concepts to both technical teams and executive stakeholders effectively.

Display familiarity with modern cloud computing platforms such as AWS, GCP, or Azure, along with experience in CI/CD practices and production deployment processes.

Have a clear understanding of how the performance of delivery directly impacts the health of commercial accounts and client satisfaction.

Commit to adhering to the guidelines and protocols that govern successful project execution and client trust.

Nice to have

Bring experience with production-level applications of large language models, vector search technologies, retrieval-augmented generation, or agentic systems.

Highlight a background in technical consulting or professional services, providing insights into client needs and project execution.

Show knowledge of MLOps tools like MLflow, Weights & Biases, or SageMaker, enhancing the efficiency of machine learning workflows.

Demonstrate awareness of enterprise security, data privacy, and compliance requirements, ensuring that projects adhere to necessary regulations.

Have experience in scaling delivery standards and practices within a rapidly growing organization, contributing to its overall success.

Practical notes

This is a remote position located within the United States.

The role includes benefits such as a stipend for remote work and a monthly lifestyle allowance to support your work-life balance.

This position requires a unique ability to balance high-level technical oversight with hands-on code reviews, ensuring both strategic direction and practical execution.

Candidates must be authorized to work in the United States without sponsorship for this position.

The role demands a proactive approach to identifying and resolving issues before they impact the client experience.