Machine Learning Engineer
About the job
- 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
- 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
About the company
Squared.ai builds and deploys AI-powered solutions for businesses by integrating predictive and generative AI into business applications. It bridges data science and business teams to enable rapid AI projects and adoption without long development cycles. The platform prepares foundational models and LLMs, enables real-time integration with mission-critical apps, and provides user-friendly interfaces plus real-time performance monitoring. Its goal is to democratize AI-driven decision-making by delivering timely AI-powered insights within existing web apps and workflows.
Company Size
51-200
Company Stage
Series A
Total Funding
$19.8M
Headquarters
Washington DC, District of Columbia
Founded
2021
Get referred to AI Sqaured
See people who can refer or advise you
Simplify's Take
What believers are saying
- January 2026 growth funding expanded product, go-to-market, and federal investment.
- May 2026 John Snow Labs partnership strengthens regulated-industry credibility in healthcare.
- Multiwoven acquisition and IBM Agent Connect deepen distribution into enterprise workflows.
What critics are saying
- Bolt competes directly with OpenAI, Anthropic, and Google on enterprise document workflows.
- Sparx’s no-code SMB pitch invites copycats from Salesforce, Microsoft, and ServiceNow.
- If federal and regulated customers slow spending, AI Squared’s platform narrative loses urgency.
What makes AI Sqaured unique
- AI Squared embeds AI inside existing workflows, not standalone dashboards.
- August 2025 Sparx deploys production-ready AI in under an hour.
- April 2026 Bolt cuts document-processing costs to one-twentieth of frontier models.
Help us improve and share your feedback! Did you find this helpful?
Benefits
- Hybrid Work Options
- Flexible Work Hours