Senior Forward Deployed Engineer (FDE)

About the role

The Senior Forward Deployed Engineer (FDE) serves as a trusted advisor to customers, combining software engineering, AI/ML expertise, technical leadership, and solution architecture to rapidly deliver innovative, production-ready solutions.

Responsibilities

• Partner directly with customer stakeholders to understand business objectives, operational challenges, and technical requirements.
• Translate complex customer needs into scalable AI-enabled software solutions.
• Lead technical discovery sessions, architecture workshops, and solution design discussions.
• Collaborate with customer leadership, product owners, architects, and engineering teams to drive successful project outcomes.
• Serve as a trusted technical advisor throughout solution implementation and deployment.
• Build and maintain strong customer relationships while managing expectations and ensuring successful delivery.
• Guide customers in adopting AI technologies and modern engineering best practices.
• Mentor engineering teams and promote technical excellence across engagements.
• Design, develop, test, and deploy AI-enabled applications, intelligent automation solutions, and modern software platforms.
• Build scalable backend services, APIs, integrations, and workflows using modern software engineering practices.
• Develop solutions utilizing large language models (LLMs), retrieval-augmented generation (RAG), agentic AI, machine learning, and other emerging AI technologies where appropriate.
• Design secure, scalable, and maintainable architectures that balance performance, reliability, governance, and cost.
• Deliver clean, production-quality, well-tested, and well-documented code.
• Implement CI/CD pipelines, automated testing, monitoring, and observability.
• Develop reusable frameworks, software components, documentation, and engineering accelerators that improve delivery across projects.
• Lead technical workstreams and provide engineering leadership across multiple projects.
• Mentor junior and mid-level engineers through design reviews, code reviews, and technical coaching.
• Contribute to proposals, technical presentations, demonstrations, proof-of-concepts (POCs), and customer solution workshops.
• Help establish engineering standards, best practices, and reusable intellectual property across the organization.
• Stay current on emerging AI technologies, cloud platforms, software engineering trends, and industry best practices.

Requirements

• Bachelor's degree in Computer Science, Software Engineering, Data Science, Information Systems, or a related technical discipline (or equivalent experience).
• 5+ years of experience in software engineering, AI engineering, machine learning, data engineering, or related technical roles.
• Experience designing and developing modern software applications using one or more programming languages such as Python, Java, C#, JavaScript, or TypeScript.
• Experience designing, building, and deploying AI/ML or Generative AI solutions in production environments.
• Experience leading technical workstreams and translating complex business requirements into scalable technical solutions.
• Experience developing REST APIs, microservices, distributed systems, or enterprise integrations.
• Experience working with cloud platforms such as AWS, Microsoft Azure, Google Cloud Platform, or hybrid cloud environments.
• Experience with CI/CD pipelines, version control, automated testing, and DevOps practices.
• Strong customer-facing communication, presentation, and consulting skills.
• Proven ability to lead technical teams in fast-paced, agile delivery environments.
• Ability to travel as required by project needs.

Qualifications

• Nice to have:
• Experience developing Generative AI applications using LLMs, Retrieval-Augmented Generation (RAG), agentic AI, intelligent automation, or multi-agent systems.
• Experience with vector databases, embeddings, prompt engineering, model evaluation, AI governance, or AI safety practices.
• Experience implementing MLOps or LLMOps practices including model deployment, monitoring, evaluation, and lifecycle management.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Experience with modern data engineering technologies including Spark, Airflow, Kafka, streaming platforms, or enterprise data architectures.
• Experience integrating enterprise systems through APIs, event-driven architectures, or messaging platforms.
• Familiarity with security, privacy, governance, and compliance best practices.
• Experience supporting federal, healthcare, defense, or other regulated industry customers.
• Experience contributing to business development activities, technical proposals, solution demonstrations, or proof-of-concepts.
• Experience mentoring engineers and building reusable engineering frameworks, accelerators, or technical assets.