ML / AI Engineer (Cloud AI & ML Ops)
Machine Learning/AI Engineer who builds and operates cloud-based ML and AI services end to end — data pipelines, training/inference, monitoring, and MLOps automation (IaC, containers, CI/CD) — for a healthcare company. Core stack is Python plus a modern cloud (AWS/Azure/GCP), on a small fully remote team.
- Compensation
- $125K–$135K / yr
- Location
- US
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Solid software engineering background — clean design, testing, maintainable code
- Proficiency in Python
- Experience with modern cloud environments (AWS, Azure, or GCP)
- Understanding of scalable architectures — containers, APIs, workflow orchestration, automation
- Experience with ML pipelines, data workflows, or AI service integrations
- Ability to work independently, prioritize effectively, and deliver in dynamic settings
- Must be a U.S. Citizen or U.S. Permanent Resident (Green Card holder) — no exceptions
Nice to have
- Familiarity with regulated or security-sensitive environments (e.g., healthcare, finance)
- Exposure to modern AI platforms and APIs, or interest in generative and conversational AI
- Knowledge of ML model monitoring, explainability, or continuous training pipelines
Full description
We are seeking a Machine Learning and AI Engineer with a strong software engineering foundation and a deep interest in AI systems. You’ll work within a small, dynamic team designing and operationalizing cloud-based ML and AI services that drive real healthcare impact.
This role demands adaptability, technical curiosity, and fast learning — the technology stack evolves constantly, and you’ll be exposed to many different tools, frameworks, and architectural patterns. You’ll contribute across the full lifecycle: data pipelines, model orchestration, infrastructure automation, and production integration.
Key Responsibilities
- Build and maintain end-to-end ML and AI pipelines for training, inference, and monitoring.
- Develop and integrate cloud-native services that host, scale, and automate AI workloads.
- Extend the company’s MLOps framework through infrastructure-as-code, containerization, and CI/CD practices.
- Collaborate with data engineers and software developers to optimize data flows and model delivery.
- Implement secure and auditable architectures that align with healthcare compliance standards.
- Participate in AI R&D initiatives, quickly learning and applying emerging technologies and tools.
- Contribute to documentation, engineering standards, and team knowledge sharing.
What We’re Looking ForCore Skills:
- Solid background in software engineering, with emphasis on clean design, testing, and maintainable code.
- Proficiency in Python and experience with modern cloud environments (AWS, Azure, or GCP).
- Understanding of scalable architectures — containers, APIs, workflow orchestration, and automation.
- Experience with ML pipelines, data workflows, or AI service integrations.
- Ability to work independently, prioritize effectively, and deliver in dynamic settings.
Mindset:
- Learns fast and adapts easily to new tools, frameworks, and technologies.
- Cloud-first thinking — naturally comfortable working with managed services and distributed systems.
- Curious and analytical — enjoys experimenting and optimizing performance.
- Collaborative — values communication and alignment with data, product, and compliance teams.
Preferred Experience:
- Familiarity with regulated or security-sensitive environments (e.g., healthcare, finance).
- Exposure to modern AI platforms and APIs, or interest in generative and conversational AI.