Lead Forward Deployed Engineer (FDE)

LTS

1d•Remote

About The Position

LTS is seeking a Lead Forward Deployed Engineer (FDE) to provide technical leadership for the design, development, and deployment of AI-powered software solutions that address complex business and mission challenges. This highly customer-facing role combines AI engineering, software architecture, solution delivery, and technical program leadership while remaining hands-on throughout the development lifecycle. The ideal candidate is an experienced engineering leader who can guide multidisciplinary teams, influence executive stakeholders, and deliver enterprise-scale AI solutions in fast-paced environments. As a Lead Forward Deployed Engineer, you will serve as the primary technical advisor to customers, establish engineering strategy, oversee solution delivery, mentor engineering teams, and help shape LTS's AI engineering capabilities.

Requirements

Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related technical discipline (or equivalent experience).
7+ years of experience in software engineering, AI engineering, machine learning, data engineering, solution architecture, or related technical roles.
Demonstrated experience leading engineering teams delivering enterprise software solutions.
Experience designing, building, and deploying AI/ML or Generative AI solutions in production environments.
Experience architecting distributed systems, APIs, microservices, cloud-native applications, or enterprise integrations.
Strong experience with cloud platforms such as AWS, Microsoft Azure, Google Cloud Platform, or hybrid cloud environments.
Experience leading technical workstreams and translating business objectives into scalable technology solutions.
Experience implementing CI/CD pipelines, DevOps practices, automated testing, and production monitoring.
Excellent communication, leadership, consulting, and stakeholder management skills.
Proven ability to lead engineering teams in agile environments while remaining hands-on technically.
Ability to travel as required by project needs.

Nice To Haves

Experience architecting Generative AI applications utilizing LLMs, Retrieval-Augmented Generation (RAG), agentic AI, intelligent automation, or multi-agent systems.
Experience establishing AI governance, evaluation frameworks, prompt engineering standards, model monitoring, and responsible AI practices.
Experience implementing MLOps or LLMOps capabilities, including model deployment, monitoring, lifecycle management, and performance evaluation.
Experience with modern data engineering platforms including Spark, Airflow, Kafka, streaming technologies, or enterprise data architectures.
Experience with containerization and orchestration technologies such as Docker and Kubernetes.
Experience integrating enterprise systems using APIs, event-driven architectures, and messaging platforms.
Familiarity with cybersecurity, privacy, governance, and regulatory compliance requirements.
Experience leading distributed engineering teams across hybrid or global delivery models.
Experience supporting federal, healthcare, defense, or other regulated industry customers.
Experience contributing to business development, technical strategy, solution architecture, and organizational engineering leadership.

Responsibilities

Serve as the primary technical advisor for customer engagements, building trusted relationships with executive leadership, technical stakeholders, and business owners.
Lead technical discovery sessions, solution workshops, and architecture discussions to define customer objectives and implementation strategies.
Translate business priorities into scalable AI-enabled software solutions and technical roadmaps.
Advise customers on AI adoption strategies, solution architecture, governance, and operational readiness.
Communicate complex technical concepts, trade-offs, and implementation risks to both technical and non-technical audiences.
Support business development activities through technical presentations, demonstrations, proof-of-concepts (POCs), proposals, and solution briefings.
Lead cross-functional engineering teams responsible for delivering enterprise AI and software solutions.
Provide technical direction throughout the full software development lifecycle, from architecture through deployment and operational support.
Guide engineering teams in designing secure, scalable, maintainable, and high-performing software systems.
Lead architecture reviews, design discussions, sprint planning, technical decision-making, and engineering governance.
Establish engineering standards, development practices, quality metrics, and delivery processes across engagements.
Remove technical blockers, manage delivery risks, and ensure successful execution across multiple projects or workstreams.
Mentor engineers through architecture guidance, code reviews, technical coaching, and career development.
Architect and oversee the development of AI-enabled applications, intelligent automation solutions, and modern software platforms.
Lead the design and implementation of solutions leveraging large language models (LLMs), retrieval-augmented generation (RAG), agentic AI, machine learning, and other emerging AI technologies.
Guide the design of scalable data pipelines, knowledge retrieval systems, APIs, microservices, and enterprise integrations.
Establish best practices for prompt engineering, AI evaluation, model governance, human-in-the-loop workflows, and responsible AI.
Define engineering approaches that balance scalability, performance, security, reliability, governance, and cost.
Review production-quality code and ensure adherence to software engineering best practices.
Drive implementation of CI/CD pipelines, automated testing, monitoring, logging, observability, and operational excellence.
Develop reusable frameworks, technical accelerators, documentation, and engineering standards that improve delivery across programs.
Lead multiple engineering initiatives while ensuring consistent delivery quality across teams.
Coordinate cross-functional and geographically distributed engineering teams, including hybrid onshore/offshore delivery models.
Establish delivery governance, resource planning, risk management, stakeholder communications, and project health reporting.
Contribute to organizational engineering strategy, AI capability development, and technical innovation.
Promote continuous improvement through engineering best practices, reusable assets, and knowledge sharing.
Stay current on emerging AI technologies, cloud platforms, software engineering trends, and industry best practices.