Source Code Technologies LLC

Lead and manage large-scale SAP APO or Kinaxis transformation programs across multiple geographies ensuring delivery excellence. | Proven leadership in SAP APO or Kinaxis program management with strong stakeholder engagement and ability to manage multi-vendor enterprise transformations. | Program Manager - SAP APO or Kinaxis Introduction: We are seeking an accomplished Enterprise Program Manager with proven experience in SAP APO or Kinaxis and enterprise-wide transformations. This role requires leadership across multiple geographies, strong stakeholder engagement, and the ability to manage large-scale, multi-vendor programs in a fast-paced environment. The Program Manager will provide executive visibility, governance, and assurance of delivery excellence. Responsibilities: • Lead multi-technology rollouts across multiple geographies, ensuring standardization and alignment with corporate objectives. • Oversee SAP integrations to enable a harmonized enterprise data and systems landscape. • Drive large-scale transformation programs in multi-vendor environments, ensuring scope clarity, collaboration, and vendor accountability. • Plan and execute transition management activities, enabling smooth handover to operations and post go-live stabilization. • Demonstrate resilience under pressure with the ability to deliver under aggressive schedules. Requirements: Required Skills: • Clarity • Collaboration • Leadership • SAP APO • Stakeholder Engagement • Transition Management Preferred Skills: No preferred skills listed.

Collaborate with cross-functional teams to gather and document business and system requirements ensuring compliance with Pharma GxP standards and support AI-enabled quality processes. | 8+ years experience in Pharma/Life Sciences business analysis with strong knowledge of GxP, FDA regulations, data governance, and experience in regulated environments. | Business Analyst – Pharma/GxP/Data Governance/Gen AI Location: Remote Experience: 8+ Years Domain: Pharma / Life Sciences Job Description We are seeking an experienced Business Analyst with strong Pharma/Life Sciences domain expertise to support initiatives focused on Data Quality, Regulatory Compliance, and Generative AI (Gen AI) within GxP-regulated environments. The ideal candidate will work closely with Quality, Regulatory, Data Science, and Technology teams to drive compliant digital transformation and AI-enabled quality processes. Key Responsibilities • Collaborate with Quality, Regulatory, and business stakeholders to gather, analyze, and document business and system requirements aligned with Pharma GxP standards. • Translate business requirements into BRDs, FRDs, URS/FRS documents, user stories, and acceptance criteria. • Drive initiatives related to data governance, data integrity, and data quality management based on ALCOA+ principles. • Identify and support Gen AI use cases including deviation analysis, CAPA automation, audit readiness, and document intelligence. • Partner with Data Science and AI teams to define model inputs/outputs, validation requirements, explainability standards, and compliance controls. • Facilitate workshops and discussions with cross-functional teams including QA, Regulatory Affairs, IT, Data Engineering, and Analytics. • Support Computer System Validation (CSV) activities and ensure compliance with regulatory requirements. • Develop and maintain traceability matrices, process flows, SOP-aligned documentation, data lineage, and mapping documents. • Coordinate and support UAT, validation cycles, and defect management activities. • Act as a liaison between business stakeholders, technical teams, and AI/analytics teams in a regulated environment. Required Qualifications • 8+ years of experience as a Business Analyst in Pharma or Life Sciences environments. • Strong understanding of GxP, FDA regulations, CSV, and validation processes. • Experience in Quality Management, Data Governance, and Data Quality initiatives. • Hands-on experience with BRD, FRD, URS, FRS, RTM, and UAT documentation. • Knowledge of ALCOA+ principles and data integrity frameworks. • Experience working with cross-functional teams in regulated environments. • Excellent communication, stakeholder management, and analytical skills. • Exposure to AI/Gen AI projects in Pharma or regulated domains is highly preferred. Preferred Skills • Experience with Quality systems, CAPA, deviations, and audit processes. • Familiarity with Agile/Scrum methodologies. • Understanding of AI governance, risk, and compliance frameworks. • Experience with pharma data platforms and analytics initiatives.

Lead architectural transformation and design enterprise-scale distributed systems using .NET and cloud technologies. | 10-15+ years software engineering with 5+ years architecture experience and expertise in .NET Core, REST APIs, cloud, and messaging systems. | Job Title: .NET Application Architect W2- Position/Remote Experience Required : 10 15+ years in software engineering 5+ years in architecture roles Strong experience in enterprise-scale distributed systems Technical Skills Backend .NET Core / .NET 6 8 REST API design, OAuth2, JWT authentication API Gateway, rate limiting, versioning DDD, CQRS, event sourcing Frontend Angular (v10+) or React Component-driven architecture State management (NgRx, Redux, etc.) UI design systems and reusable components Accessibility (WCAG), responsive design Databases SQL Server / Sybase (design, indexing, performance tuning) NoSQL: MongoDB, DynamoDB, CosmosDB Cloud & DevOps Azure / AWS / Google Cloud Platform Docker, Kubernetes, Helm CI/CD tools: Azure DevOps, Jenkins, TeamCity, Octopus Messaging & Integration Kafka, RabbitMQ, Azure Service Bus, Event Hub Event-driven and streaming architectures Caching & Performance Redis / Distributed caching Scalability, failover, resiliency design Testing & Quality xUnit, NUnit, MSTest Moq, FakeItEasy, NSubstitute Cypress, Playwright, Jest Observability Splunk, ELK, App Insights, Grafana, Prometheus Git workflows & branching strategies Leadership Competencies Proven ability to drive architectural transformation Strong decision-making and problem-solving skills Excellent stakeholder communication Mentorship and team development mindset Thought leadership in cloud adoption and modernization

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.