Senior AI / Machine Learning Engineer – Clinical Decision Support Systems (CDSS)

@KarthikTechCon

Hi, I’m Karthik, a Senior AI / Machine Learning Engineer with 10+ years of experience, including clinical AI and production-grade decision support systems. I’ve built and deployed risk prediction models for sepsis, diabetes, and cardiac events using XGBoost, LightGBM, CatBoost, with a strong focus on explainability, safety, and regulatory compliance. What I bring: • Clinical risk modeling with real-world EHR / HIS data • Explainable AI (SHAP/LIME) with confidence scores • Time-series patient data handling • Human-in-the-loop controls to reduce alert fatigue • Validation using AUROC, sensitivity, specificity, FP rates • Integration via HL7 / FHIR Regulatory mindset: • Experience with SaMD-style documentation, audit trails, and data privacy (PDPL-style) • Bias and drift monitoring for clinical safety Tech stack: Python, FastAPI, Docker, MLflow I strongly believe in trustworthy, explainable clinical AI that supports—never replaces—physicians. Regards, Karthik Senior AI / ML Engineer 10+ Years Experience

@abdf2010

Hello there, I reviewed your project Senior AI / Machine Learning Engineer – Clinical Decision Support Systems (CDSS) and understood the requirements at a high level. I focus on delivering clear, stable, and maintainable solutions aligned with the actual scope, I can work with Python, Machine Learning (ML), Docker and follow a clean development process with proper structure and error handling. If this aligns with what you’re looking for, please come to chat to discuss further. Best regards

@chiragardeshna

Hi there, I am a strong fit for this role because I have led production-grade clinical AI systems where explainability, safety, and regulatory readiness were as critical as model accuracy. I have hands-on experience building and deploying disease risk prediction models using XGBoost, LightGBM, and CatBoost on real-world EHR data, including time-dependent vitals and labs. I work extensively with Python, scikit-learn, SHAP, and ML pipelines designed for hospital environments and clinician workflows. I have implemented explainable CDSS outputs with contributing factors, confidence scores, and human-in-the-loop controls to reduce alert fatigue. I reduce risk by running silent-mode validations, tracking AUROC and false positives, monitoring drift, and maintaining full audit trails aligned with healthcare governance. I am comfortable working with HL7 and FHIR data and supporting compliance with SFDA SaMD, PDPL, and SDAIA principles. I am available to engage full time and start immediately. Regards Chirag

@Web1devloper

As a seasoned AI/Machine Learning engineer, I have amassed over 7 years of industry experience with an emphasis on clinical data and healthcare AI. Over my career, I have not only gained in-depth knowledge about essential AI technologies like XGBoost, LightGBM, and CatBoost, but I have also developed specialized skills in implementing explainable artificial intelligence (SHAP / LIME), which aligns perfectly with your expectations from this role. Additionally, my experience working with volatile health records (HL7 & FHIR) while maintaining regulatory compliance (like the SFDA and PDPL) further stamps my suitability for the role. What truly sets me apart from the competition is my past work switch similar CDSS, where I've successfully developed and deployed clinical models that targeted sepsis warning and diabetes/cardiac risk stratification - two prime focus areas of your project. I firmly believe that in healthcare AI isn't just about raw accuracy - it's about ensuring transparency to avoid false alerts and enhance clinician's overall effectiveness while working with AI-driven systems. My past work validated this perspective by integrating human-in-the-loop decision controls to avoid alert fatigue- a crucial factor for successful implementation.

@prashjain92

Hello, I carefully read the full role description and expectations and truly appreciate the emphasis on safe, explainable, and clinically responsible artificial intelligence. Building systems that physicians can trust in real hospital environments requires both technical depth and strong respect for clinical workflows, and this role strongly aligns with that mindset. I understand the need to design and deploy disease risk prediction models using real world hospital data, ensure explainability for every prediction, collaborate closely with clinicians, and meet strict validation and regulatory requirements within a clinical decision support system. Clinical risk prediction models, explainable insights for clinicians, healthcare data integration, validation and monitoring. I have 7+ years of experience in artificial intelligence and machine learning, including hands on work with predictive modeling, data pipelines, deployment, monitoring, and governance. I focus on building reliable systems that balance accuracy with safety, transparency, and long term trust. Let us schedule a quick chat to align your vision and next steps. Best Regards, Prasham Jain

@himanshus642

Having dedicated over 5 years to constructing and implementing AI-powered solutions, I am well-prepared to handle this high-impact project. My proficiency lies in designing real-world AI models with explainable outputs that are both clinically-thresholded and regulatory compliant. Building on solid experience with healthcare data (including EHR/ HIS via HL7 & FHIR), I'm particularly skilled at handling missing, noisy, and time-dependent data --perfect for reducing false alerts and optimizing model performance. Furthermore, I maintain a robust understandi of SFDA, PDPL, and SDAIA's AI governance principles, which will be invaluable when maintaining compliance and ensuring stringent clinical evaluation and audit documentation. One of the qualities distinguishing me in this niche is my Full-Stack Engineering expertise, especially proficiency in using Docker, FastAPI, MLflow , which adds leverage to my AI/Machine Learning skillset. Notably, I've built platforms that interface between multiple systems ensuring smooth workflow automation and efficient cloud deployment (with AWS). My strong suit does not just lie in the technical aspects; it extends prominently in the capacity to deliver business-driven solutions by aligning technology with concrete business objectives. Beyond that is the promise of long-term technical support. Join me as we build groundbreaking regulatory-grade clinical AI together - because what sets us apart matters!

@Solutionzhere

I have a comprehensive understanding of the complexities involved in developing and deploying clinical decision support systems (CDSS) in the healthcare domain. My seven years of experience, including three years handling healthcare data, ideally situates me to lead this critical and impactful project. I am already familiar with the challenges inherent in working with real-world clinical data such as missing, noisy, time-dependent variables which are critical to this job given its use in predicting diseases like sepsis, diabetes, and cardiac events. My expertise extends beyond just using AI and ML algorithms. I have extensive knowledge in the regulatory requirements demanded by Saudi Arabia including SFDA (SaMD) regulations, Saudi PDPL, SDAIA AI governance principles among others. As part of my deliverables for this project, I assure proper documentation for clinical evaluations and audit trails for AI decisions that has to maintain full compliance within these regulations. Let's talk more over chat.