Do your healthcare AI engineers have BAA experience?

Yes. Every Taction healthcare AI engineer has worked under a Business Associate Agreement on PHI-bearing systems. We maintain a tracked list of BAA-eligible model providers and update it quarterly.

Custom Software

Hire Dedicated Healthcare AI Engineers

Healthcare AI in 2026 is no longer a research project. Hospitals, payers, and digital health companies are shipping production AI features inside Epic, generating clinical documentation at the point of care, automating prior authorization, and predicting readmissions before discharge. None of that gets to production with a generalist AI engineer who has never signed a BAA, never read HIPAA §164.312, and never debugged a hallucinated MedicationRequest.

Taction Software’s healthcare AI engineers have shipped production AI features inside Epic, Cerner, Athena, and MEDITECH. They have built RAG pipelines on FHIR data, fine-tuned models on de-identified clinical notes, deployed on-prem LLMs in hospital data centers, and stood up eval harnesses that catch clinical drift before it reaches a patient. 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 by Industry Leaders Worldwide

Industries and Use Cases We Have Delivered

Hospital systems — clinical copilots, ambient documentation, predictive analytics, AI triage

Health systems IT — AI inside Epic via SMART on FHIR, EHR write-back patterns

Digital health startups — RAG-based patient education, AI clinical decision support, AI-driven RPM

Payers — prior authorization automation, claim denial prediction, member engagement AI

Pharma and CROs — patient-trial matching, adverse event detection, eCRF data quality

MedTech — AI features on FDA SaMD pathway, FHIR write-back from medical devices

Recent production work includes ambient documentation for a 12-clinic group, an AI triage copilot for an emergency department, and a predictive cardiac RPM engine.

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Awards & Recognitions

Why Healthcare AI Engineers Are Not Interchangeable with Generic AI Engineers

A generic AI engineer can fine-tune a model, build a RAG pipeline, and deploy a chatbot. None of that survives contact with a real clinical environment.

In healthcare, every AI feature has to clear seven gates that do not exist anywhere else:

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What We Screen For Before Placement

Every Taction healthcare AI engineer is screened on five criteria:

Production LLM deployment experience — at least one shipped feature using OpenAI, Anthropic Claude, AWS Bedrock, or Google Vertex AI in a regulated environment
RAG or fine-tuning pipeline experience — vector database selection, embedding strategy, retrieval evaluation, citation grounding
FHIR R4 fluency — the AI layer has to talk to the data layer, no exceptions
HIPAA-grade engineering habits — BAA-aware deployment, PHI redaction at inference, audit logging, encryption discipline
Eval harness experience — clinical accuracy, safety, fairness, calibration, and drift metrics, not just task accuracy

What a Taction Healthcare AI Engineer Does on Day One

You get a dedicated engineer, not a pool, embedded in your engineering team for a minimum 3-month engagement billed monthly at $8K. They join your standups, work in your repos, and report into your engineering lead.

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Week One and Two Deliverables

In the first two weeks, a typical Taction healthcare AI engineer will:

Map your AI feature scope against BAA-eligible model providers and recommend the deployment path
Stand up a development environment with PHI redaction at the inference boundary
Implement the first end-to-end inference call, prompt template, and output schema
Wire audit logging that captures user, model version, prompt template hash, output, and override per HIPAA §164.312(b)
Build the first eval harness skeleton with clinical accuracy and safety metrics

By week six, that engineer is shipping production AI code that has passed your eval harness, your security review, and a clinician usability test.

Technologies Our Healthcare AI Engineers Ship in Production

Foundation Models and LLM Providers

OpenAI — GPT-4o, GPT-4o-mini, o-series reasoning models (BAA via Azure OpenAI)
Anthropic — Claude Sonnet, Claude Opus, Claude Haiku (BAA via AWS Bedrock or direct)
AWS Bedrock — multi-model with BAA coverage
Google Vertex AI — Gemini family with BAA
Microsoft Azure OpenAI — GPT family with BAA
On-prem open models — Llama, Mistral, Mixtral on hospital-owned hardware

RAG and Vector Infrastructure

Pinecone, Weaviate, Qdrant, pgvector, Elasticsearch
LangChain, LlamaIndex
Embedding strategy with OpenAI, Cohere, or open embedding models
Citation-grounded retrieval patterns for clinical reasoning

MLOps and Production Engineering

Model versioning, deployment, monitoring, drift detection
Eval harness frameworks for clinical accuracy, safety, fairness, calibration
Prompt template versioning and A/B testing
Inference observability with PHI-aware logging

Healthcare Data Stack

FHIR R4 integration for AI input and output
HL7 v2 to FHIR transformation via Mirth Connect
OMOP CDM for research workloads
De-identification pipelines

Clinical AI Patterns We Have Shipped

Ambient clinical documentation with EHR write-back
AI triage copilots inside emergency departments
Predictive RPM with deterioration scoring
AI medical coding and clinical documentation improvement
Prior authorization automation
HL7 message routing with LLM classification

For deeper background, read our coverage of building ambient clinical documentation, the healthcare AI development companies 2026 buyer’s guide, and the HIPAA-compliant AI engineers playbook.

Engagement Models and Pricing for Healthcare AI Engineers

Dedicated Healthcare AI Engineer

$8,000 per engineer per month. Minimum 3-month commitment, month-to-month thereafter. Full-time, dedicated, embedded in your team. Includes BAA, project-management overhead, and Taction technical-architect oversight. This is the default model.

Multi-Engineer Healthcare AI Pod

$24,000 to $80,000 per month for a pod of 3 to 8 engineers including a lead AI architect and a clinical SME. Use this when you have parallel workstreams — for example, RAG pipeline, eval harness, EHR integration, and clinical UX running concurrently.

Fixed-Scope Healthcare AI Sprint

When scope is well-defined, our healthcare AI Discovery Sprint at $45K over 4 weeks, MVP Sprint at $95K over 8 weeks, or Pilot-Ready Sprint at $145K over 12 weeks fixes the price and the deliverable.

For a project-based estimate, use the healthcare AI cost calculator or the LLM inference cost calculator.

HIPAA and AI Compliance Baseline

Every Taction healthcare AI engagement starts with the same baseline.

BAA executed before any access to PHI-bearing systems and before any model provider receives PHI in inference

BAA-eligible model providers only — we maintain a tracked list and update it quarterly

PHI redaction at inference for any cloud model path

Audit logging at the model call layer, capturing user, model version, prompt template, output, and override

Eval harness with clinical accuracy, safety, fairness, and calibration metrics

Drift monitoring with retraining triggers

Encryption at rest with AES-256 and in transit with TLS 1.3

When to Hire a Healthcare AI Engineer (and When Not To)

Use a Dedicated Healthcare AI Engineer When

You are building production AI features inside Epic, Cerner, Athena, or another major EHR
You need RAG over clinical data, fine-tuning on de-identified notes, or both
You are deploying on-prem LLMs in a hospital data center
You are a digital health company that needs HIPAA-grade AI before your next funding round or pilot
You are a payer or CRO building AI on regulated clinical data

Choose a Different Engagement When

You have not scoped the AI use case yet — start with our healthcare AI Discovery Sprint for a $45K scoping engagement
You need only an LLM engineer with no FHIR or EHR component — hire LLM engineers for healthcare
You need MLOps specialization specifically — hire healthcare MLOps engineers
Your project is an FDA SaMD pathway — hire FDA SaMD engineers for regulatory specialization

The 14-Day Process to Hire a Healthcare AI Engineer

Day 0: Discovery Call

30 minutes with a Taction healthcare AI lead. We map your AI use case, EHR targets, data sources, BAA constraints, and team structure.

Days 1 to 5: BAA and MSA

Legal paperwork runs in parallel with technical scoping. We pre-sign on our side. Turnaround depends on your legal team.

Days 3 to 10: Engineer Match

We propose 2 to 3 candidate engineers with use-case-specific experience matched to your scope — RAG, fine-tuning, ambient documentation, predictive analytics, or eval harness. You interview each.

Days 10 to 14: Onboarding

Selected engineer joins your standups, gets access to your repos and dev environment, signs your individual confidentiality agreement, and starts the technical onboarding plan co-authored with your engineering lead.

Start the 14-Day Engineer Match

FAQs

Frequently Asked Questions About Hiring Healthcare AI Engineers

$8,000 per engineer per month for a dedicated healthcare AI engineer with a minimum 3-month commitment. Multi-engineer pods, fixed-scope sprints, and quarterly Care Packages are also available. For project-based estimates, use the healthcare AI cost calculator.

14 days from initial discovery call to engineer-on-team for standard engagements. The bottleneck is usually BAA and MSA paperwork on the client side.

Yes. Every Taction healthcare AI engineer has worked under a Business Associate Agreement on PHI-bearing systems. We also maintain a tracked list of BAA-eligible model providers (OpenAI via Azure, Anthropic via Bedrock, Google Vertex AI, on-prem open models) and update it quarterly. For deeper context, see our guide on BAAs with AI providers.

Yes. We have deployed Llama, Mistral, and Mixtral on hospital-owned hardware behind a firewall with no outbound calls to public AI APIs. For the cost and architecture details, see our analysis of on-prem LLM hardware for healthcare.

A healthcare AI engineer is an LLM engineer who has also shipped against HIPAA, FHIR, and clinician-trust UX requirements. If your project is LLM-only with no healthcare-specific constraints, hire LLM engineers for healthcare is the appropriate page. If you need full healthcare AI fluency including EHR integration and compliance, this page is the right starting point.

Yes. Healthcare AI engineers build the feature. MLOps engineers operate it in production — versioning, deployment, monitoring, retraining. Many engagements need both. If you are scaling an existing healthcare AI feature to production, hire healthcare MLOps engineers is the specialized page.

Some AI features cross the FDA Software-as-a-Medical-Device threshold and require 510(k) pathway prep. Taction healthcare AI engineers know how to spot that line during discovery. If your feature is on the SaMD pathway, we recommend pairing the AI engineer with a regulatory specialist — see our FDA SaMD pathway package.