Source Code Technologies LLC

Lead product security lifecycle activities, assess security architecture, develop security baselines, collaborate with teams, and support cloud-native and connected product security initiatives. | 5-10 years product security experience, strong public cloud skills (AWS, Azure), healthcare/MedTech experience, knowledge of secure SDLC, and strong communication skills. | Product Security / Software Engineer Cloud & Security | Healthcare / MedTech 100% Remote Contract Role Healthcare / MedTech experience required Overview We are seeking a Product Security / Software Engineer with a strong background in Cloud Security, Product Security, and Healthcare/MedTech environments. This role will lead and execute product security lifecycle activities across BD s STS (Software Technology Solutions) portfolio, including standalone software, integration services, connected medical devices, and next-gen AI-driven cloud platforms. This is a hands-on, high-impact position working across R&D, Product Teams, and Corporate Security. Responsibilities • Own product security lifecycle activities across BD s STS product portfolio. • Conduct assessment of current security architecture and create remediation roadmaps. • Develop, maintain, and optimize product security baselines. • Collaborate with R&D, Product Security leadership, and engineering teams to ensure alignment on security goals. • Define and implement security controls aligned with industry best practices and regulatory requirements. • Drive security tooling initiatives: selection, automation, process integration, and portfolio-wide adoption. • Build and maintain metrics/dashboards to provide clear, data-driven insights into product security status. • Support cloud-native and connected product security initiatives including medical IoT and edge device security. Required Skills & Experience • 5 10 years of Product Security experience • Strong experience with public cloud environments (AWS, Azure required; multi-cloud preferred) • Knowledge of Windows Server, secure SDLC, and engineering methodologies • Experience in regulated industries; Healthcare/MedTech strongly preferred • Understanding of Connected Products / Medical IoT / Edge Device Management • Ability to work independently with minimal oversight; proactive and results-driven • Strong communication & documentation skills; ability to operate in a matrix organization Nice to Have • Experience securing AI-driven cloud platforms • Security certification(s) such as CISSP, CCSP, CEH, GIAC • Familiarity with secure architecture frameworks and medical regulatory standards

Design, implement, and deploy scalable cloud-native ML pipelines and production AI solutions using Azure, Docker, and AKS with strong MLOps practices. | 8+ years experience as a Data Scientist with 3+ years in ML engineering on cloud, proficiency in Azure ML, Docker, AKS, Python, SQL, Linux, and strong MLOps knowledge. | AI/ML Engineer Location Mundelein IL Duration 3 months (Only Independent contractors) This is a Hybrid role (3 days onsite at Mundelein IL) As an AI Engineer on the Data Science team, you will play a key role in productionizing machine learning models, building robust pipelines, and enhancing the overall AI platform. This role requires hands-on experience with Azure, Docker,and Azure Kubernetes Service (AKS), as well as strong knowledge of cloud-native MLOps best practices. Responsibilities • Design and implement scalable, cloud-native ML pipelines for production AI solutions. • Collaborate with data scientists to operationalize ML models from prototypes to production. • Manage deployment of ML models using Azure Machine Learning and AKS. • Develop, containerize, and orchestrate services using Docker and Kubernetes. • Optimize cloud data and compute architectures to ensure cost-effective and reliable deployments. • Implement robust monitoring, logging, and CI/CD practices to support AI operations (MLOps). • Work closely with enterprise cloud architects to align AI solutions with client s infrastructure standards. • Contribute to the evolution of the best practices around AI/ML systems in production environments. Qualifications • Minimum 8 years of experience as a Data Scientist, with at least 3 years focused on machine learning engineering in cloud environments. • Proven experience deploying ML models in Azure, preferably with Azure Machine Learning, Docker, and AKS. • Hands-on experience building cloud-native pipelines for model training, scoring, and monitoring. • Familiarity with GenAI concepts and tools (experience operationalizing GenAI is a plus). • Proficiency in Python, SQL, and Linux-based development environments. • Strong understanding of MLOps principles, CI/CD pipelines, and production-grade APIs. • Effective communicator with strong problem-solving skills and ability to work across teams. Education • Bachelor s degree in Computer Science, Electronic Engineering, Data Science, or a related field.