Kapi

Work Arrangement: Dallas-based / Hybrid

Visa Sponsorship: Not available. Candidates must already be authorized to work in the United States without current or future employer sponsorship.

Job Summary

We are seeking a highly experienced Senior MLOps Engineer with strong hands-on Snowflake experience to support enterprise machine learning platforms and production ML workloads.

The ideal candidate has hands-on experience taking machine learning models from experimentation through production and building the deployment pipelines, monitoring, automation, infrastructure, and governance capabilities required to operate ML solutions reliably at enterprise scale.

This role will work closely with Data Scientists, ML Engineers, Data Engineers, Cloud Engineers, and enterprise platform teams.

Key Responsibilities

• Design, build, and maintain enterprise-grade MLOps platforms and pipelines.
• Operationalize machine learning models developed by Data Science teams.
• Build automated ML workflows covering training, validation, deployment, monitoring, retraining, and retirement.
• Implement CI/CD pipelines specifically for machine learning workloads.
• Establish model registry, versioning, lineage, artifact management, and reproducibility.
• Implement model monitoring, data drift detection, model drift detection, prediction-quality monitoring, and alerting.
• Integrate ML workloads with Snowflake-based enterprise data environments.
• Build and optimize Python- and SQL-based data and ML pipelines.
• Support Snowflake data ingestion, transformation, compute, security, and ML integrations.
• Containerize ML workloads using Docker and deploy workloads through Kubernetes or comparable orchestration platforms.
• Implement logging, observability, alerting, and production support processes.
• Automate deployment and infrastructure provisioning using modern DevOps and Infrastructure-as-Code practices.
• Support model governance, approval workflows, lineage, auditability, and access controls.
• Troubleshoot production ML pipelines, model-serving infrastructure, Snowflake integrations, and performance issues.
• Develop reusable MLOps frameworks, standards, templates, and best practices.

Mandatory Qualifications

Candidates must have hands-on production experience in both MLOps and Snowflake.

MLOps – Required

Strong production experience with:

• ML model deployment and operationalization
• Model lifecycle management
• ML CI/CD
• Experiment tracking
• Model registry and versioning
• Automated model validation
• Model monitoring
• Data and model drift detection
• Retraining pipelines
• Pipeline orchestration
• Production troubleshooting

Experience with one or more of the following:

• ML flow
• Kubeflow
• AWS SageMaker
• Azure Machine Learning
• Airflow
• Argo Workflows
• Prefect
• Dagster
• Equivalent enterprise MLOps platforms

Snowflake – Required

Strong hands-on Snowflake experience including:

• Snowflake architecture
• Databases, schemas, tables, and views
• Virtual warehouses
• Compute management
• Snowflake security and RBAC
• Data ingestion and transformation
• Performance optimization
• Python integration
• Snowflake integration with ML pipelines

Experience with the following is strongly preferred:

• Snowpark
• Snowpark Python
• Snowflake ML
• Snowflake Model Registry
• Snowflake Feature Store
• Snowflake Tasks and Streams
• Dynamic Tables
• Snowpipe
• Cortex / Snowflake AI capabilities

Additional Required Technical Skills

• Strong Python
• Strong SQL
• Git
• REST APIs
• Linux
• Shell scripting
• Docker
• CI/CD
• Cloud platforms such as AWS, Azure, or GCP

Preferred Skills

Experience with:

• Kubernetes
• Terraform
• GitHub Actions
• Jenkins
• GitLab CI/CD
• Azure DevOps
• dbt
• Spark
• Kafka
• Grafana
• CloudWatch
• Evidently

Education and Experience

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Machine Learning, or related field.
• 6+ years of software, cloud, data, or ML engineering experience.
• 3+ years of hands-on production MLOps experience.
• Strong hands-on Snowflake experience.
• Experience deploying ML models into production.
• Experience implementing ML CI/CD pipelines.
• Strong Python and SQL skills.
• Experience with Docker and cloud infrastructure.

Work Authorization

This position does not provide visa sponsorship.

Candidates must be currently authorized to work in the United States without employer sponsorship and must not require sponsorship now or in the future.

About KAPI Advisors LLC
KAPI Advisors LLC is a forward-thinking boutique firm at the intersection of AI, Machine Learning, Data Analytics, Generative AI, Mathematical Optimization, and Cloud Platform Engineering. We build highly personalized, cutting-edge solutions tailored to the specific needs of each client — empowering organizations with powerful tools to unlock new business opportunities, operational efficiencies, and platform reliability at scale.
At KAPI Advisors, we are passionate about solving complex, real-world challenges through technology. Our capabilities span intelligent agent-based systems, advanced data analytics, optimization algorithms, and robust cloud infrastructure — all designed to work together seamlessly. Our offerings include Generative AI systems that enable automation and intelligent decision-making, ML models that surface actionable insights, Mathematical Optimization techniques for supply chain management, resource allocation, and logistics, and Site Reliability Engineering practices that ensure the platforms powering these solutions remain performant, resilient, and production-ready.
We believe that great AI and data products are only as strong as the infrastructure beneath them. That's why our SRE and DevOps practice is central to everything we build — from designing observability frameworks and incident response playbooks, to implementing chaos engineering, disaster recovery, and developer productivity tooling that reduces toil and accelerates delivery.
As a boutique firm, we offer the agility and depth that larger organizations simply cannot match. Our team works directly with clients to understand their unique challenges and craft tailored strategies that deliver both immediate impact and long-term resilience. Whether you're looking to scale an AI platform, modernize your cloud infrastructure, or build the reliability engineering culture your engineering team deserves — KAPI Advisors is the partner built for it.