Comprehensive Rehab Consultants
Machine Learning Engineer
Rol remoto de Machine Learning Engineering con fit claro de ubicación del candidato.
Publicado21 jun 2026
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Pakistán
Resumen del rol
Machine Learning Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
Machine Learning & Statistical Modeling
- Build, tune, and validate statistical models including multi-stage regression, ordered probit, and generalized linear models, audit automation, acuity scoring, and financial forecasting
- Engineer features from structured and unstructured healthcare data (EMR, claims, revenue cycle, clinician notes)
NLP & LLM Engineering
- Tune the existing CRC NLP engine for clinical note understanding, keyword extraction, concept expansion, negation detection, and sentiment scoring
- Build custom clinical embeddings using HuggingFace Transformers, spaCy, and domain-tuned vector models
- Develop and maintain a CRC private LLM, trained on internal knowledge bases, documentation, analytics logic, and care guidelines
- Build automated pipelines for LLM evaluation, retraining, retrieval-augmented generation (RAG), and grounded QA
AI-Driven Product Development
- Architect, build, and deploy the AI Analytics Chatbot, integrating model logic, business rules, and Fabric/Databricks data sources
- Integrate ML models into production services using notebooks, APIs, or batch inference jobs
- Support creation of AI-generated reporting, insights summaries, and automated clinical/financial narratives
MLOps & Engineering
- Build maintainable ML pipelines (training, validation, deployment) using Databricks, Fabric, MLflow, GitHub, and CI/CD
- Implement model monitoring, drift detection, and automated retraining
- Package and deploy reproducible models via APIs or scheduled Fabric/Databricks workflows
Collaboration & Delivery
- Work with data engineering to embed models into CRC applications
- Partner with BI analysts to transform model outputs into dashboards
- Document methodologies, assumptions, architecture, and validation processes clearly
Required Skills & Experience
- 3–6 years of hands-on machine learning engineering experience (not just DS notebooks)
- Strong Python engineering background: pandas, scikit-learn, statsmodels, PyTorch or TensorFlow, transformers, spaCy
- Experience building and tuning LLM and NLP pipelines end-to-end
- Experience with regression, ordered probit/logit, hierarchical models, and general statistical modeling
- Experience deploying ML workloads in Databricks, Azure ML, and Fabric
- Strong SQL for feature engineering and model validation
- Prior experience working with healthcare data (EMR, claims, RCM, CMS) preferred
- Strong communication and the ability to explain complex ML systems to non-technical stakeholders.
- Proactive, self-managing engineer who can independently own ML systems end-to-end.
- Fluent English required
Preferred Qualifications
- Experience with:Retrieval-Augmented Generation (RAG) pipelinesVector databases (FAISS, Chroma, Pinecone, Qdrant)Enterprise chatbot frameworksMLflow, CI/CD, GitHub Actions, and model versioningPower BI integration for ML outputsFHIR/SMART on FHIR
- Retrieval-Augmented Generation (RAG) pipelines
- Vector databases (FAISS, Chroma, Pinecone, Qdrant)
- Enterprise chatbot frameworks
- MLflow, CI/CD, GitHub Actions, and model versioning
- Power BI integration for ML outputs
- FHIR/SMART on FHIR
Details
- Retrieval-Augmented Generation (RAG) pipelines
- Vector databases (FAISS, Chroma, Pinecone, Qdrant)
- Enterprise chatbot frameworks
- MLflow, CI/CD, GitHub Actions, and model versioning
- Power BI integration for ML outputs
- FHIR/SMART on FHIR
Certifications considered:
- Databricks ML Associate/Professional
- Azure AI Engineer Associate
- DeepLearning.AI NLP/LLM specializations
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