Ai/ml Software Engineer (US)

About the JobThis position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI/ML Software Engineer based in United States.

This role sits at the intersection of AI engineering, data science, and full-stack software development. You will design, build, and evolve production-grade AI systems, including agentic workflows and autonomous solutions. Working across the technology stack, you will develop robust backend services, intuitive interfaces, and data pipelines that bring AI capabilities into real-world applications. You will contribute to multi-agent orchestration, prompt engineering, model evaluation, and production optimization. The role combines hands-on software development with experimentation and applied research to solve complex technical and business challenges. You will collaborate with engineers, customers, and cross-functional stakeholders in an agile environment where ambiguity and innovation are part of the work. Your contributions will directly support the development of advanced technologies with meaningful applications in the future of flight.

Accountabilities AI and agentic systems: Design, build, and maintain agentic workflows, autonomous systems, and multi-agent architectures that automate complex tasks and enhance product capabilities. Production AI development: Integrate AI and machine learning models into scalable, reliable production applications, going beyond model training to address prompts, validation, deployment, and operational performance. Full-stack engineering: Develop clean, maintainable software across the technology stack, including backend services, user-facing interfaces, APIs, and supporting application components. AI orchestration: Implement orchestration logic for multi-agent systems and task automation using appropriate frameworks and architectural patterns. Data pipelines and evaluation: Build prototype-level data pipelines supporting model training, evaluation, review, and data movement between AI components and applications. Model performance and risk management: Monitor and optimize AI systems in production while identifying and mitigating issues such as hallucinations, bias, reliability, and performance degradation. Cross-functional collaboration: Partner with engineers, customers, and business stakeholders to understand requirements, translate needs into technical solutions, and provide effective technical support. Technical innovation: Research and apply emerging developments in AI, LLMs, prompt engineering, and agentic architectures to improve products and user experiences. Agile delivery: Contribute throughout the software development lifecycle, from experimentation and prototyping through implementation, deployment, and continuous improvement.

Requirements Education: Bachelor’s degree in Computer Science or a STEM-related discipline. Experience: At least 3 years of professional experience in AI/ML engineering. AI/ML expertise: Strong understanding of AI/ML concepts, LLMs, prompt engineering, and practical AI application development. Software engineering: Proven full-stack engineering experience with strong programming skills, particularly in Python, along with tools such as JupyterHub, GitHub, and Visual Studio. Agentic AI: Experience designing or building agentic workflows, autonomous systems, or multi-agent architectures. AI frameworks: Familiarity with orchestration frameworks such as LangChain, AutoGen, CrewAI, or comparable technologies. Cloud and infrastructure: Experience with cloud platforms such as AWS, Azure, or GCP, as well as containerization technologies including Docker and Kubernetes. Solution architecture: Ability to contribute to scalable solution architecture and integrate AI capabilities into production-grade software systems. Data and model evaluation: Experience creating data pipelines and validating AI model performance through data analysis and structured evaluation. Problem solving: Ability to navigate ambiguous, complex challenges and apply AI/ML techniques to practical, real-world problems. Customer orientation: Ability to interact effectively with customers, understand their needs, and provide appropriate technical guidance and support. Professional profile: A valid LinkedIn profile is requested. Work authorization: Candidates must be legally authorized to work in the United States without current or future employment visa sponsorship. Because…