Featured
What We Screen For Before Placement
- Production MLOps shipping experience
- Drift detection and monitoring — at least one shipped feature
- Eval pipeline engineering
- HIPAA-grade deployment habits
Custom Software
Hire Dedicated Healthcare MLOps Engineers in the USA
Building a clinical AI model is the easy part. Operating one in production — versioning, deploying, monitoring for drift, retraining, eval-harnessing, and meeting HIPAA audit requirements for every model call — is where most healthcare AI projects fail. MLOps for healthcare is its own engineering discipline, more rigorous than consumer MLOps and more demanding than standard data engineering.
Taction Software’s healthcare MLOps engineers have shipped production MLOps platforms for clinical decision support, ambient documentation, predictive RPM, and AI medical coding. Every engineer has worked under a BAA, with model versioning, drift detection, and eval pipelines that survive audit. Engagements start at $8,000 per engineer per month with a 14-day onboarding window and a Business Associate Agreement signed before any PHI touches our systems.
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Trusted Partners
Trusted by Industry Leaders Worldwide
Industries and Use Cases We Have Delivered
Hospital systems — clinical AI feature operations
Digital health — AI product MLOps at scale
MedTech — FDA SaMD lifecycle management for ML models
Pharma and CROs — RWE and biomarker model operations
Recognition
Awards & Recognitions
Why Healthcare MLOps Is Harder Than Standard MLOps
What a Taction Healthcare MLOps Engineer Does on Day One
Featured
Week One and Two Deliverables
- Audit your existing model lifecycle and identify gaps
- Stand up model versioning and reproducible build pipeline
- Wire drift detection on a production model
- Build the eval harness skeleton
- Wire audit logging per HIPAA §164.312(b)
Technologies Our Healthcare MLOps Engineers Ship in Production
MLOps Stack
- MLflow, Weights & Biases, Comet
- Kubeflow, Vertex AI, SageMaker
- Triton Inference Server, vLLM
- Model registry with lineage and reproducibility
Drift and Monitoring
- Data drift, concept drift, label drift detection
- Evidently AI, WhyLabs, Arize
- Custom drift logic for clinical features
- Retraining trigger engineering
Eval Pipelines
- Task accuracy benchmarking
- Clinical accuracy and safety metrics
- Fairness and calibration metrics
- Citation grounding for LLM features
Deployment Infrastructure
- AWS SageMaker, Azure ML, Vertex AI
- On-prem Kubernetes for hospital-hosted models
- Inference observability with PHI-aware logging
For background, see our coverage of healthcare AI MLOps patterns and the HIPAA-compliant AI engineers playbook.
Engagement Models and Pricing for Healthcare MLOps Engineers
Dedicated Healthcare MLOps Engineer
$8,000 per engineer per month with a 3-month minimum.
MLOps Pod
$24,000 to $48,000 per month for a pod of 3 to 6 engineers.
For estimates, use the healthcare AI cost calculator and see our eval harness build add-on.
HIPAA Compliance Baseline
- BAA executed with Taction and all infrastructure subprocessors
- Audit logging at every model call with lineage
- Encryption at rest with AES-256 and in transit with TLS 1.3
- FDA SaMD lifecycle readiness where applicable
When to Hire a Healthcare MLOps Engineer (and When Not To)
01
Use a Dedicated Healthcare MLOps Engineer When
- You have AI features in production and need ops rigor
- You are scaling from one model to many
- You are preparing models for FDA SaMD submission
- You need drift detection and retraining triggers
- You need eval pipeline engineering
02
Choose a Different Engagement When
- You need to build the AI feature first — hire healthcare AI engineers or LLM engineers
- You need clinical data pipeline work — hire clinical data engineers
- You need FDA SaMD pathway specifically — hire FDA SaMD engineers
The 14-Day Process
FAQs
Frequently Asked Questions About Hiring Healthcare MLOps Engineers
$8,000 per engineer per month with a 3-month minimum.
14 days from discovery to engineer-on-team.
Yes. Data drift, concept drift, and label drift detection are part of the standard MLOps stack we ship.
Yes. We pair MLOps engineers with FDA SaMD engineers when the model is on the SaMD pathway. See also our FDA SaMD pathway package.
MLflow, Weights & Biases, Kubeflow, SageMaker, Vertex AI, Azure ML, plus on-prem Kubernetes for hospital-hosted models.
Yes. This is core MLOps work. See our eval harness build add-on for the productized version.
Yes. Every engagement begins with a BAA.