Senior ML Engineer – Consultant

Visa

. Deploy and productionize machine learning models developed by data science teams .

Posted 10/1/2026full-timeAustin • Texas • United StatesSenior💰 $163,400 - $261,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in deploying and optimizing machine learning models in production environments, with a strong focus on MLOps practices, model monitoring, and performance evaluation. Proficient in programming and utilizing cloud platforms and tools to ensure scalable and reliable ML solutions.

Highest-signal resume keywords

MLOpsModel DeploymentMachine Learning OptimizationCloud PlatformsModel Monitoring

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Hard Skills

PythonJavaScalaC++C#GoRustMachine Learning ConceptsAPIsData Pipelines

Tools & Technologies

MLflowKubeflowSageMakerVertex AIDatabricksTensorFlowPyTorchDockerKubernetesCI/CD

Industry Keywords

Model ServingFeature EngineeringA/B TestingData DriftOperational SLAsBig Data TechnologiesExplainabilityGovernanceComplianceGenerative AI

Tech Stack

Tools & technologies

CloudDistributed SystemsDockerHadoopJavaKafkaKubernetesPythonPyTorchRustScalaScikit-LearnSparkTensorflowC++Go

About the role

Key responsibilities & impact

Deploy and productionize machine learning models developed by data science teams
Build and maintain model deployment pipelines, including packaging, testing, versioning, release management, and rollback processes
Optimize models and inference services for latency, throughput, scalability, reliability, cost, and resource efficiency
Support batch, streaming, real-time, and API-based model serving environments
Partner with data scientists to understand model logic, features, dependencies, validation metrics, and expected production behavior
Translate model artifacts and technical specifications into production-ready code and services
Implement monitoring for model performance, data quality, feature drift, model drift, latency, availability, and prediction quality
Support model validation, A/B testing, champion/challenger testing, and controlled rollout strategies
Contribute to MLOps capabilities such as CI/CD, model registries, feature stores, orchestration, observability, and automated testing
Troubleshoot production issues related to model serving, data pipelines, infrastructure, and performance
Ensure production ML solutions meet requirements for security, compliance, explainability, auditability, and operational resilience
Work with data scientists, engineers, product teams, and platform teams to move models from experimentation into scalable, reliable, and secure production systems
Travel 5-10% of the time

Requirements

What you’ll need

8 or more years of relevant work experience with a Bachelor Degree, or at least 5 years of experience with an Advanced Degree, or 2 years of work experience with a PhD
Bachelor’s Degree in Computer Science, Engineering, Machine Learning, Statistics, Operations Research, Mathematics, or a related quantitative field, or equivalent experience
Experience deploying, operationalizing, or supporting machine learning models in production environments
Strong programming experience in one or more languages such as Python, Java, Scala, C++, C#, Go, or Rust
Understanding of machine learning concepts, including model training, feature engineering, validation, evaluation, and performance metrics
Experience building production software, APIs, data pipelines, or distributed systems
Experience with cloud platforms, containerized applications, or scalable data/ML infrastructure
Advanced Generative AI experience or usage
Strong experience with MLOps, model deployment, model serving, model monitoring, and production ML systems
Experience optimizing ML models or inference pipelines for latency, throughput, cost, scalability, and reliability
Experience with tools and platforms such as MLflow, Kubeflow, SageMaker, Vertex AI, Databricks, TensorFlow, PyTorch, scikit-learn, or XGBoost
Experience with Docker, Kubernetes, CI/CD pipelines, cloud platforms, and observability tools
Experience with batch scoring, real-time inference APIs, streaming pipelines, or feature pipelines
Experience monitoring for data drift, model drift, prediction quality, availability, and operational SLAs
Experience with A/B testing, canary deployments, blue/green deployments, or champion/challenger model frameworks
Experience working with large datasets and big data technologies such as Spark, Kafka, Snowflake, Hadoop, Hive, or Databricks
Familiarity with modeling techniques such as logistic regression, decision trees, gradient boosting, neural networks, SVM, Naïve Bayes, or Bayesian methods
Experience with explainability, governance, auditability, compliance, and post-deployment model integrity
Ability to work with emerging technologies such as Generative AI tools, including ChatGPT and Microsoft Copilot

Benefits

Comp & perks

Medical insurance
Dental insurance
Vision insurance
401(k)
FSA/HSA
Life Insurance
Paid Time Off
Wellness Program
Bonus eligibility
Equity eligibility