Senior/Lead Forward Deployed Engineer (FDE) - AI-Native Development

At Valtech, you’ll find an environment designed for continuous learning, meaningful impact, and professional growth. Whether you're pioneering new digital solutions, challenging conventional thinking, or building the next generation of customer experiences, your work will help transform industries.

We are seeking a Senior/Lead Forward Deployed Engineer (FDE) with a strong software engineering background, architecture experience, and an AI-native mindset. In this highly client-facing role, you will partner directly with enterprise customers to solve complex business and technical challenges, leveraging modern AI-native development practices to accelerate software delivery and innovation.

This role is ideal for engineers who are already using AI as part of their daily workflow to plan, design, develop, test, and deploy software. Rather than focusing exclusively on building AI products, you will help customers deliver business outcomes by applying AI-assisted development tools, agentic workflows, and modern software engineering practices across a variety of cloud platforms and technical environments.

Successful candidates are experienced problem-solvers who can navigate ambiguity, work across multiple technologies and programming languages, and confidently integrate systems, data sources, and cloud services to deliver scalable solutions.

This role is ideal for engineers who enjoy operating at the intersection of technical leadership, client partnership, and hands-on AI delivery. Successful candidates are not only exceptional builders but also trusted collaborators who can help customers navigate ambiguity, unlock business value, and accelerate their transition to becoming AI-native organizations.

This position is remote and may require occasional travel.

Role Responsibilities

• Serve as the technical lead for customer engagements, aligning business objectives, engineering priorities, and technology opportunities to deliver measurable outcomes.
• Partner with executive sponsors, product leaders, and engineering teams to define technology roadmaps and drive adoption of AI-native ways of working.
• Lead cross-functional workshops and solution design sessions, facilitating alignment between technical and non-technical stakeholders.
• Act as a trusted advisor while remaining hands-on in implementation, balancing strategic leadership with direct contribution to architecture, coding, and deployment.
• Champion an AI-native engineering culture by promoting responsible AI practices, AI-assisted development workflows, automation, and continuous experimentation.
• Mentor customer engineers and internal teams on modern software development practices, AI-native delivery approaches, architecture patterns, and operational best practices.
• Embed within customer engineering teams and lead technical discovery, stakeholder alignment, and solution planning sessions with business stakeholders, engineering leadership, product teams, and security partners to translate ambiguous business challenges into scalable technology solutions and delivery roadmaps.
• Utilize AI-assisted engineering tools, including Claude Code, Cursor, Codex, and similar platforms, to rapidly design, build, test, and optimize software solutions while improving engineering productivity and delivery quality.
• Design and implement scalable software solutions that integrate applications, services, APIs, databases, and enterprise platforms across complex technology ecosystems.
• Architect and deliver modern software systems, leveraging AI-native development practices to accelerate planning, design, development, testing, deployment, and operations.
• Work across multiple cloud platforms, development frameworks, programming languages, and enterprise environments to deliver solutions tailored to customer needs.
• Design and implement data integrations and pipelines utilizing structured and unstructured data sources, ensuring reliable access, governance, and performance.
• Build and maintain the integration layer between custom software solutions and enterprise systems, including APIs, legacy platforms, identity providers, security controls, and data services.
• Develop observability, monitoring, and evaluation frameworks to ensure software quality, performance, reliability, and operational excellence.
• Troubleshoot and resolve complex production issues across distributed systems, applications, integrations, and cloud environments.
• Co-build with customer engineering teams and act as a hands-on leader and advocate for AI-native engineering practices, introducing AI-assisted development workflows, automation techniques, and modern engineering approaches that accelerate delivery and improve effectiveness.
• Evaluate emerging AI tools, frameworks, and engineering practices and recommend practical ways to improve customer delivery outcomes.
• Drive a deliberate handoff to the customer’s team, ensuring long-term ownership, documentation, knowledge transfer, and adoption after the engagement concludes.
• Develop and maintain technical documentation, architecture decision records, solution designs, and implementation best practices across all assigned engagements.

Must Have Qualifications

• Strong software development background with experience designing, building, and delivering production-grade applications and platforms.
• Experience leveraging AI-assisted development tools and agentic workflows to accelerate software engineering activities, including solution design, development, testing, debugging, and delivery.
• Demonstrated AI-native mindset, regularly incorporating AI into development workflows to improve productivity, software quality, and delivery velocity.
• Architecture and systems integration experience, including designing solutions that connect applications, services, APIs, databases, and enterprise systems.
• Experience working with structured and unstructured data sources, databases, and enterprise data ecosystems.
• Ability to navigate diverse technology stacks, programming languages, cloud environments, and customer requirements to solve complex technical problems.
• Experience using AI and agentic principles to build software products and accelerate engineering outcomes, rather than exclusively developing AI products themselves.
• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• Experience deploying cloud resources via Terraform or similar infrastructure-as-code tools.
• Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI requirements and translate ambiguous business goals into technical roadmaps.
• Experience integrating AI systems with enterprise IT infrastructure, including authenticated APIs, legacy data systems, and corporate security perimeters.
• Ability to travel up to 25% of the time to customer sites.
• AI proficiency for productivity.
• Outstanding communication, leadership, and client engagement skills, including the ability to influence executive stakeholders, facilitate decision-making, and explain complex AI and architectural concepts to both technical and non-technical audiences.

Nice to Have Qualifications

• Master’s degree or PhD in AI, Computer Science, Machine Learning, or a related technical field.
• Experience implementing advanced agentic AI systems, including multi-agent architectures, tool orchestration, planning frameworks, and autonomous workflows.
• Experience designing and deploying Model Context Protocol (MCP) servers, tool integration frameworks, and extensible connector ecosystems that support enterprise-scale AI applications.
• Strong understanding of operational excellence for large-scale AI platforms, including observability, tracing, performance optimization, cost management, and monitoring of LLM-powered systems.
• Experience supporting and troubleshooting mission-critical AI applications in production environments with high user volumes and stringent reliability requirements.
• Experience architecting AI solutions within complex enterprise environments, including secure governance, compliance, data sovereignty, and regulated or restricted infrastructure requirements.
• Knowledge of human-centered AI design principles, including explainability, transparency, trust, and effective user experiences for AI-powered products.
• Proven ability to lead large-scale AI transformation initiatives that improve engineering productivity, accelerate software delivery, or drive measurable business outcomes.
• Experience championing AI-assisted software development practices, including the adoption of coding assistants, developer productivity tooling, and engineering best practices.
• Familiarity with modern application architecture, API development, cloud-native design patterns, and distributed systems.
• Cloud Platform Certifications:
• Google Cloud Professional Cloud Architect
• Google Cloud Professional Data Engineer
• Google Cloud Professional Machine Learning Engineer
• AWS Certified Solutions Architect
• AWS Certified Developer
• AWS Certified Machine Learning Engineer
• Microsoft Certified: Azure Solutions Architect Expert
• Microsoft Certified: Azure AI Engineer Associate
• Microsoft Certified: Azure Data Engineer Associate
• AI Certifications:
• Anthropic Claude Developer Certification
• Anthropic Claude Architect Certification
• Gemini Enterprise Certification
• Other recognized AI platform certifications