Forward Deployed AI Engineer 2026 - US, COL, UK

Aimpoint Digital is a market-leading data, AI, analytics, and operations research advisory and solution engineering firm. We help organizations design, build, and operationalize enterprise-grade data and AI platforms, decision intelligence solutions, optimization systems, and production AI applications.

We are not a commodity consulting firm. We are a technical partner to organizations that need to move beyond experimentation and deploy AI into real business workflows with the architecture, governance, reliability, and engineering discipline required for production.

We believe successful AI initiatives require much more than prompting an LLM. They require strong software engineering, scalable architecture, reliable deployment, thoughtful evaluation, and disciplined engineering practices.

About the Role

As a Forward Deployed AI Engineer, you will work directly with clients to design, build, deploy, and scale enterprise AI solutions. You will combine strong software engineering fundamentals with modern AI technologies to deliver production-ready systems that create measurable business value.

You will work across the entire AI engineering lifecycle; from solution architecture and application development to deployment, evaluation, and operationalization.

What You Will Do

• Design, build, and deploy production AI applications, copilots, retrieval systems, and agentic workflows
• Translate business problems into scalable technical solutions using modern AI engineering best practices
• Develop backend services, APIs, and application architectures that integrate AI capabilities into enterprise systems
• Build multi-agent systems, AI agents, workflow automation, and decision-support systems
• Deploy AI solutions into production with appropriate security, observability, monitoring, evaluation, and governance
• Design AI systems that integrate with enterprise data platforms, APIs, databases, messaging systems, and business applications
• Collaborate with cross-functional client teams including engineering, data, product, architecture, security, and business stakeholders
• Experience deploying and operating containerized applications on Kubernetes, including scaling, service networking, resource management, and production monitoring
• Contribute reusable accelerators, frameworks, technical assets, and thought leadership that strengthen the AI Engineering practice
• Stay current with emerging AI technologies and recommend practical approaches that improve client outcomes

What We Are Looking For

We are looking for engineers who enjoy solving difficult technical problems and building production software. You are comfortable moving between software engineering, AI application development, cloud architecture, and client collaboration.

You understand that successful AI systems require excellent engineering, not just excellent models.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or equivalent practical experience
• 3+ years of professional software engineering experience building production applications
• 1+ years designing and deploying AI or machine learning solutions into production
• Strong programming experience using Python, Java, C#, Go, TypeScript, or similar languages
• Experience building scalable backend systems, APIs, or distributed applications.
• Experience developing AI applications using modern LLMs, machine learning models, or intelligent automation solutions
• Experience with online and offline evaluation, observability, context engineering, guardrails, and AI governance
• Experience with MLOps, LLMOps, or production deployment pipelines
• Strong understanding of software engineering principles, including testing, version control, CI/CD, code reviews, and system design
• Experience using Claude Code, OpenAI Codex, Google Antigravity, Cursor, GitHub Copilot, or other comparable coding harnesses
• Experience integrating AI applications with enterprise data sources, APIs, and business systems
• Familiarity with cloud platforms such as AWS, Azure, GCP, Databricks, Snowflake, or similar technologies
• Experience deploying applications using containers, Kubernetes, serverless platforms, or similar cloud-native technologies
• Strong communication skills with the ability to explain technical concepts to both technical and non-technical stakeholders
• Ability to independently own technical workstreams while collaborating across multidisciplinary teams…