MLOps Platform Architect

Tiger Analytics

full-time

Posted on: 10/16/2025

Location Type: Remote

Location: Remote • 🇺🇸 United States

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SeniorLead

AWSAzureCloudDockerGoogle Cloud PlatformKubernetesPython

About the role

- Architect and Build MLOps Platforms: Lead the design and development of end-to-end MLOps platforms from the ground up, ensuring they are scalable, reliable, and secure.**
- Client Management: Act as a primary technical point of contact for clients, managing expectations, communicating complex technical concepts clearly, and navigating challenging project requirements to ensure successful outcomes.**
- Technical Leadership: Drive the technical vision for the MLOps practice, establishing best practices for model development, deployment, monitoring, and governance.**
- Databricks Expertise: Leverage extensive, hands-on experience with Databricks to build and optimize data and machine learning pipelines.**
- Cloud Integration: Design and implement solutions on one or more major cloud platforms (AWS, GCP, or Azure), utilizing their native services for data, compute, and machine learning.**
- Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and business stakeholders, to deliver integrated and high-value solutions.

- 13+ years of professional experience in data engineering, machine learning, or software architecture, with a significant focus on MLOps.**
- Proven, hands-on experience in building and deploying production-grade MLOps platforms.**
- Demonstrated ability to handle challenging client management scenarios, acting as a trusted advisor and problem-solver.**
- Strong expertise with the Databricks ecosystem for building scalable data and ML workflows.**
- Extensive experience with at least one major cloud platform (AWS, GCP, or Azure) and its MLOps-related services.**
- Deep understanding of the entire machine learning lifecycle, from data ingestion and feature engineering to model serving and monitoring.**
- Proficiency in programming languages such as Python and experience with relevant ML libraries.**
****Preferred Qualifications:****
- A background in traditional software development or software engineering principles.**
- Experience with containerization (Docker) and orchestration (Kubernetes).**
- Certification in a relevant cloud platform (e.g., AWS Certified Machine Learning - Specialty, Google Cloud Professional Machine Learning Engineer)

Benefits

Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility.
***Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.***

Tip: use these terms in your resume and cover letter to boost ATS matches.

MLOpsdata engineeringmachine learningPythonDatabricksDockerKubernetescloud platformsmodel servingfeature engineering

client managementtechnical leadershipcommunicationproblem-solvingcollaboration

AWS Certified Machine Learning - SpecialtyGoogle Cloud Professional Machine Learning Engineer