Navon Tech
Navon Tech is a specialized consultancy dedicated to empowering organizations with trusted data and AI ecosystems that drive business value, mitigate risk, and foster innovation. We partner with Fortune 500 enterprises and high-growth companies to implement operational data governance, AI risk management, and regulatory compliance programs. With expertise in data strategy, governance, and AI risk frameworks, we deliver pragmatic solutions tailored to modern enterprise complexities. Our focus lies in enterprise data governance frameworks, AI governance, and regulatory compliance, ensuring organizations derive maximum value from their data assets. Located at the intersection of data, AI, and compliance, Navon Tech is recognized for its actionable, results‑driven solutions and trusted thought leadership.
Role Summary Design, implement, and operate HIPAA‑compliant, production‑grade MLOps platforms enabling AI use cases from pilots to MVPs and enterprise scale. Responsibilities include model lifecycle management, data/feature pipelines, observability & auditability, governance alignment (NIST AI RMF, FDA GMLP), and secure cloud deployment patterns on Kubernetes.
Location Preferred onsite (DFW / Chicago). Ideal candidate can be considered for remote.
Key Responsibilities
Data & Feature Engineering: Build governed pipelines for FHIR/HL7, LOINC, X12 with data quality checks.
Observability & Drift Monitoring: Use OpenTelemetry, Prometheus/Grafana, Evidently AI.
Model Governance: Enforce model cards, lineage tracking, and NIST SP 800-53 controls.
Inference & Serving: Deploy models with NVIDIA Triton, autoscaling, and canary deployments.
GenAI & RAG Productionization: Implement PHI tokenization, vector DB integration, and guardrails.
Reliability Engineering: Define SLAs/SLOs and incident response per NIST SP 800-61.
Security & Ethics: Apply Zero Trust, DPIAs, bias/explainability tooling (Fairlearn, SHAP).
Cross‑Functional Leadership: Partner with clinical, compliance, and product teams for readiness assessments.
Compliance & Ethics: Operationalize NIST AI RMF, FDA GMLP, HIPAA, and GDPR for ethical AI and risk management.
Healthcare Data & Interoperability: Implement pipelines around FHIR R4/USCDI, HL7 v2, LOINC, X12; secure EHR integration via SMART on FHIR OAuth2.
Required Qualifications
Bachelor’s/Master’s in Computer Science or related field.
8–10+ years in production ML/MLOps with 5+ years in healthcare or regulated industries.
Hands‑on with Kubernetes, Docker, Terraform, Kubeflow, MLflow, Python, CI/CD tools, and HIPAA compliance.
Experience implementing HIPAA controls and NIST SP 800-53 security measures.
Preferred Qualifications
Experience with NVIDIA Triton, Feast, Pinecone/Weaviate, LangChain, Evidently AI.
Familiarity with HITRUST, SOC 2, ISO/IEC 27001 frameworks.
Core Technical Skills
Data Engineering: Spark/Databricks, Kafka, Delta Lake, Great Expectations.
Serving/Scaling: Kubernetes (HPA), KEDA, GPU scheduling, canary deployments.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries IT System Data Services
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Role Summary Design, implement, and operate HIPAA‑compliant, production‑grade MLOps platforms enabling AI use cases from pilots to MVPs and enterprise scale. Responsibilities include model lifecycle management, data/feature pipelines, observability & auditability, governance alignment (NIST AI RMF, FDA GMLP), and secure cloud deployment patterns on Kubernetes.
Location Preferred onsite (DFW / Chicago). Ideal candidate can be considered for remote.
Key Responsibilities
Data & Feature Engineering: Build governed pipelines for FHIR/HL7, LOINC, X12 with data quality checks.
Observability & Drift Monitoring: Use OpenTelemetry, Prometheus/Grafana, Evidently AI.
Model Governance: Enforce model cards, lineage tracking, and NIST SP 800-53 controls.
Inference & Serving: Deploy models with NVIDIA Triton, autoscaling, and canary deployments.
GenAI & RAG Productionization: Implement PHI tokenization, vector DB integration, and guardrails.
Reliability Engineering: Define SLAs/SLOs and incident response per NIST SP 800-61.
Security & Ethics: Apply Zero Trust, DPIAs, bias/explainability tooling (Fairlearn, SHAP).
Cross‑Functional Leadership: Partner with clinical, compliance, and product teams for readiness assessments.
Compliance & Ethics: Operationalize NIST AI RMF, FDA GMLP, HIPAA, and GDPR for ethical AI and risk management.
Healthcare Data & Interoperability: Implement pipelines around FHIR R4/USCDI, HL7 v2, LOINC, X12; secure EHR integration via SMART on FHIR OAuth2.
Required Qualifications
Bachelor’s/Master’s in Computer Science or related field.
8–10+ years in production ML/MLOps with 5+ years in healthcare or regulated industries.
Hands‑on with Kubernetes, Docker, Terraform, Kubeflow, MLflow, Python, CI/CD tools, and HIPAA compliance.
Experience implementing HIPAA controls and NIST SP 800-53 security measures.
Preferred Qualifications
Experience with NVIDIA Triton, Feast, Pinecone/Weaviate, LangChain, Evidently AI.
Familiarity with HITRUST, SOC 2, ISO/IEC 27001 frameworks.
Core Technical Skills
Data Engineering: Spark/Databricks, Kafka, Delta Lake, Great Expectations.
Serving/Scaling: Kubernetes (HPA), KEDA, GPU scheduling, canary deployments.
Seniority Level Mid‑Senior level
Employment Type Full‑time
Job Function Engineering and Information Technology
Industries IT System Data Services
#J-18808-Ljbffr