FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in MLOps, including productionizing machine learning models, automating workflows, and enhancing operational dashboards. Proficient in Python, SQL, and cloud infrastructure, with a strong focus on reliability and customer-centric solutions.
Highest-signal resume keywords
MLOps ExperiencePython ProgrammingSQL ProficiencyKubernetes KnowledgeCI/CD Implementation
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Machine Learning OperationsModel DeploymentAPI DevelopmentData Pipeline ManagementOperational TroubleshootingModel MonitoringAutomated WorkflowsModel ValidationDebugging Across StackRelease Management
Soft Skills
Strong CommunicationJudgment in Decision-Making
Tools & Technologies
AWSKubernetesAirflowSparkMLflowCI/CD ToolsObservability ToolsFastAPIS3Hive
Industry Keywords
B2B SaaSPredictive ModelingCustomer-Facing EngineeringOperational DashboardsMulti-Tenant SaaS
Tech Stack
Tools & technologiesAirflowAWSCloudKubernetesPythonSparkSQL
About the role
Key responsibilities & impact- Productionize customer-tailored predictive models, configurations, and scoring workflows with validation, release, monitoring, and rollback paths
- Build and improve UI workflows for training triggers, model refreshes, validation outcomes, model status, and operational troubleshooting
- Enhance model dashboards covering model health, score changes, data quality, refresh status, and production issues
- Build or modify APIs, backend services, data contracts, and orchestration workflows
- Automate model retraining, validation, promotion, and deployment workflows for customer engagements
- Create safeguards for data quality, score distributions, threshold changes, and edge cases
- Monitor models and services for drift, data shifts, reliability issues, and operational regressions
- Turn repeatable customer solutions into reusable product and platform capabilities
- Build deployment templates, reusable workflow components, and configuration-driven interfaces
- Create self-service or assisted tools for training, refresh monitoring, validation review, and troubleshooting
- Improve the MLOps platform, including Kubernetes workloads, compute, pipelines, CI/CD, deployment tooling, and observability
- Establish runbooks, release checklists, audit trails, and support processes for model operations
- Identify recurring customer pain points and convert them into product requirements, platform improvements, or operational tooling
- Work closely with the MLOps team in India so customer work reaches production in days, not weeks
- Report to the MLOps engineering lead
Requirements
What you’ll need- 6+ years of experience in MLOps, ML platform engineering, backend engineering, full-stack engineering, or related production engineering roles
- Experience owning machine-learning systems in production, including training, deployment, monitoring, retraining, and incident response
- Strong Python and SQL skills
- Experience with APIs, backend services, asynchronous workflows, testing, CI/CD, and observability
- Experience building or materially contributing to internal tools, operational dashboards, or customer-facing product workflows
- Comfortable debugging across the stack: UI, APIs, workflows, data pipelines, model artifacts, and production infrastructure
- Experience releasing changes safely across multiple customers or production environments, including staged rollout, impact checks, and rollback
- Working knowledge of Kubernetes and cloud infrastructure
- Strong communication and judgment when balancing customer urgency, usability, reliability, and long-term maintainability
- Nice to have: Fast API and Frontend experience
- Airflow, Spark, MLflow, model registries, feature pipelines, or workflow orchestration
- AWS, S3, Hive, distributed compute, and multi-tenant SaaS experience
- Forward-deployed, consulting, solutions, implementation, or customer-facing engineering experience
- B2B SaaS, go-to-market data, or predictive modeling experience
Benefits
Comp & perks- Health coverage
- Paid parental leave
- Generous paid time off and holidays
- Quarterly self-care days off
- Stock options
- Equipment and support for working and connecting with teams
- Learning and development initiatives
- Access to LinkedIn Learning
- Quarterly wellness education sessions
- Wellness days
- ERG-hosted events
- 401K employer matching program
- Life insurance
- Disability insurance
- Bonus program or commission plan may be included
- Paid holidays