smino is a fast‑growing SaaS platform used by architects, planners, and construction companies to manage projects from planning to handover. The product supports seamless communication, documentation, and task management across all stakeholders in a construction project. The platform is collaborative, mobile, and designed to streamline workflows in a traditionally complex industry.
Senior MLOps Engineer
Location
United States
Posted
2 days ago
Salary
Not specified
Seniority
Senior
Job Description
Role Description
We are looking for a Senior / Strong Middle MLOps Engineer to own ML infrastructure, model deployment, and data pipelines across the platform. This is a hands-on role at the intersection of MLOps, DevOps, and Data Engineering, focused on scaling AI systems and bringing models into stable, cost-efficient production.
Responsibilities
- Build and maintain scalable ML infrastructure for training and inference
- Deploy and optimize ML models (batch and real-time)
- Work with embeddings pipelines and vector databases
- Optimize performance and cost of model deployments
- Manage Kubernetes environments (EKS/GKE or similar)
- Implement Infrastructure as Code (Terraform)
- Build and maintain ETL/ELT pipelines
- Optimize database performance (Postgres, large-scale data)
- Improve CI/CD pipelines and deployment workflows
- Implement monitoring and observability (Prometheus, Grafana)
- Collaborate with AI engineers to productionize models
Qualifications
- 3–5+ years in MLOps / DevOps / Data Engineering
- Strong Python skills
- Hands-on Kubernetes experience
- Experience with AWS or similar cloud
- Experience deploying ML models to production
- Solid CI/CD and Docker experience
- Strong SQL and database experience (PostgreSQL)
- Experience with Terraform or other IaC tools
Requirements
- Experience with large-scale inference or embeddings pipelines
- Performance and cost optimization of ML systems
- Experience with Airflow, MLFlow, Spark, or DBT
- Experience with vector DBs and RAG systems
- Exposure to LLM-based systems (LangChain, OpenAI, etc.)
Benefits
- Experience with AI-first or agent-based platforms
- Experience with multi-tenant SaaS architectures
- Multi-cloud experience (AWS + GCP)
Key Competencies
- Strong ownership and hands-on mindset
- Ability to work across MLOps, DevOps, and Data domains
- Focus on performance, scalability, and cost optimization
- Comfortable working in fast-paced startup environment
Job Requirements
- 3–5+ years in MLOps / DevOps / Data Engineering
- Strong Python skills
- Hands-on Kubernetes experience
- Experience with AWS or similar cloud
- Experience deploying ML models to production
- Solid CI/CD and Docker experience
- Strong SQL and database experience (PostgreSQL)
- Experience with Terraform or other IaC tools
- Experience with large-scale inference or embeddings pipelines
- Performance and cost optimization of ML systems
- Experience with Airflow, MLFlow, Spark, or DBT
- Experience with vector DBs and RAG systems
- Exposure to LLM-based systems (LangChain, OpenAI, etc.)
Benefits
- Experience with AI-first or agent-based platforms
- Experience with multi-tenant SaaS architectures
- Multi-cloud experience (AWS + GCP)
- Key Competencies
- Strong ownership and hands-on mindset
- Ability to work across MLOps, DevOps, and Data domains
- Focus on performance, scalability, and cost optimization
- Comfortable working in fast-paced startup environment
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