Lead AI Engineer

Aimpoint Digital

. Design, build, and deploy production AI applications, copilots, retrieval systems, and agentic workflows .

Posted 10/27/2026full-timeRemote • United States, Colombia, United KingdomSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying AI applications and solutions, with a strong foundation in software engineering principles and experience in integrating AI capabilities into enterprise systems. Proficient in collaborating with cross-functional teams to deliver scalable, production-ready applications while ensuring governance and observability.

Highest-signal resume keywords

AI Application DevelopmentBackend Services DevelopmentMLOps and LLMOpsContainerization and KubernetesStrong Programming in Python, Java, C#, Go, TypeScript

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI Engineering Best PracticesAPI DevelopmentMachine Learning SolutionsScalable Backend SystemsObservability and MonitoringSoftware Engineering PrinciplesVersion ControlCI/CDTestingSystem Design

Soft Skills

Strong Communication SkillsCollaboration Across TeamsAbility to Explain Technical Concepts

Tools & Technologies

KubernetesAWSAzureGCPDatabricksSnowflakeClaude CodeOpenAI CodexGitHub CopilotFastAPI

Industry Keywords

AI SolutionsMulti-Agent SystemsWorkflow AutomationDecision-Support SystemsEnterprise Data IntegrationCloud-Native TechnologiesDistributed ApplicationsData PipelinesSemantic SearchKnowledge Graphs

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsFlaskGoogle Cloud PlatformJavaKubernetesPythonSnowflakeSpringSpring BootSpringBootTypeScriptGo.NET

About the role

Key responsibilities & impact

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 integrating 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 engineering, data, product, architecture, security, and business stakeholders on client teams
Deploy and 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
Stay current with emerging AI technologies and recommend practical approaches that improve client outcomes

Requirements

What you’ll need

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 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 technical and non-technical stakeholders
Ability to independently own technical workstreams while collaborating across multidisciplinary teams
Preferred: experience with FastAPI, Flask, Spring Boot, .NET, Express, multi-agent systems, AI workflows, agents, semantic search, vector databases, knowledge graphs, distributed systems, concurrency, high-scale cloud applications, AI solutions serving 1000+ users, consulting or enterprise customers, data pipelines, or LLM pre-training/fine-tuning