Lead AI Engineer
New
A
Aimpoint DigitalAI engineering
Remote work within the US, COL and UKFull-TimeLead
Salary not disclosed
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Job Details
- Experience
- 3+ years of professional software engineering experience; 1+ years designing and deploying AI or machine learning solutions into production
- Required Skills
- PythonJavaKubernetesTypeScriptC#GoCI/CDLLMMLOps
Requirements
- 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.
- 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 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, or Snowflake.
- Experience deploying applications using containers, Kubernetes, serverless platforms, or similar cloud-native technologies.
Responsibilities
- Design, build, and deploy production AI applications, copilots, retrieval systems, and agentic workflows.
- Translate business problems into scalable technical solutions using modern AI engineering 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 security, observability, monitoring, evaluation, and governance.
- Integrate AI systems with enterprise data platforms, APIs, databases, messaging systems, and business applications.
- Collaborate with client engineering, data, product, architecture, security, and business teams.
- Operate containerized applications on Kubernetes, including scaling, service networking, resource management, and production monitoring.
- Contribute reusable accelerators, frameworks, technical assets, and thought leadership to the AI Engineering practice.
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