Machine Learning Engineer
Join AI Squared as a Machine Learning Engineer to operationalize and maintain scalable AI/ML systems in a hybrid work environment.
Posted by employer 11 months ago
First seen on Joblaze 1 week ago
Last verified on the company career page 1 day ago
Skills & Technologies
AI in the day-to-day
You will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform.
Requirements
- Experience
- 5+ years
Not disclosed in this posting: compensation, visa sponsorship.
Joblaze summary
In this role, the Machine Learning Engineer is responsible for deploying, maintaining, and monitoring AI and ML systems that support the company's platform. Key skills include proficiency in Python, experience with ML lifecycle tools, and expertise in cloud platforms like AWS and GCP, along with containerization and orchestration technologies. This position is ideal for someone with over five years of experience in machine learning or MLOps, who can effectively collaborate with cross-functional teams to ensure the reliability and scalability of production systems.
Joblaze insights
- Listed 9 days ago on Joblaze — tracked directly from AI Squared's career page.
- AI Squared has 7 other open roles including QA Engineer, Account Executive - Enterprise, Sales Engineer.
- 6527 active Python roles on Joblaze right now.
- 2727 active AWS roles on Joblaze right now.
- 2568 active Kubernetes roles on Joblaze right now.
- Typical Python salary we track runs $125,000–$190,800 (median $160,000, across 3229 roles). See Python salary trends
Quick facts
- Is the Machine Learning Engineer role remote?
- It's hybrid — AI Squared expects some on-site time in Washington, DC.
- How much experience is required?
- At least 5 years of relevant experience for this Machine Learning Engineer role.
- Where is the role based?
- AI Squared is hiring for this position in Washington, DC.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: AWS, Azure, Docker, GCP, Kubeflow, Kubernetes.
- What seniority level is this role?
- AI Squared targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Machine Learning Engineer role at AI Squared.
From the original posting
About the Role:
We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You’ll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.
Key Responsibilities:
- Design, implement, and maintain ML deployment pipelines for scalable production systems.
- Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
- Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
- Partner with data scientists to transition models from research/prototype into production-ready deployments.
- Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
- Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
- Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
- Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.
- 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
- Proven experience deploying and maintaining machine learning models in production at scale.
- Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
- Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
- Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
- Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
- Strong understanding of MLOps best practices, monitoring, and automation.
- Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
- Strong communication and collaboration skills across technical and non-technical teams.