• Design, train, and evaluate ML models for logistics use cases: delivery time estimation, demand forecasting, capacity planning, anomaly detection • Improve and iterate on existing production models using performance data and customer feedback • Run structured experiments to validate model improvements before promotion to production • Own the full path from trained model to production API — wrapping, deploying, versioning, and monitoring • Build and maintain inference APIs serving predictions at scale (<100ms latency targets) • Deploy and manage models on GCP Vertex AI • Implement A/B testing and rollback strategies for safe model promotion • Build real-time and batch feature pipelines from Postgres/AlloyDB sources • Design feature stores serving both training and inference • Implement data validation and quality monitoring to catch drift before it affects customers • Develop CI/CD pipelines for model deployment • Monitor model and pipeline health; own incident response for ML systems • Optimize inference costs across GCP and Cloudflare infrastructure • Partner with the Data Scientist on experiment design and feature strategy • Work with platform engineers to integrate ML outputs into core product services • Communicate model behavior, limitations, and tradeoffs clearly to non-ML engineers and product stakeholders

Description The Technical Lead, Vulnerability Management serves as a senior technical lead within the Security Modernization team, responsible for driving remediation, security modernization, and technical execution across the enterprise. This role leads cross-functional cybersecurity initiatives, ensuring vulnerabilities, misconfigurations, and other security findings are resolved efficiently and in alignment with organizational risk tolerance. Beyond vulnerability management, the Technical Lead provides technical expertise and coordination for broader cybersecurity projects, secure configuration efforts, infrastructure and cloud hardening, and on-going engineering strategies. The position reports to the Sr Dir, Enterprise Architecture, and plays a critical role in strengthening the organization’s overall security posture through disciplined, repeatable, and collaborative execution. Essential Job Functions: The Technical Lead, Vulnerability Management will demonstrate competencies across the following areas. • Oversee technical execution of cybersecurity support and maintenance activities, including vulnerability remediation, penetration test and red team findings, vendor-reported issues, and configuration hardening efforts. • Lead additional technical cybersecurity initiatives outside of vulnerability management, such as infrastructure and cloud security modernization, access hardening, endpoint security improvements, and secure configuration projects. • Serve as the primary liaison between the CTO team and the Cybersecurity organization. • Coordinate remediation and security modernization efforts across infrastructure, applications, cloud, SaaS, and hybrid environments. • Partner with IT, Engineering, and Business stakeholders to define remediation plans, timelines, technical solutions, and success criteria. • Ensure remediation tasks are completed within SLA, escalating blockers, and driving cross-functional accountability. • Build and maintain dashboards and reporting that track remediation progress, SLA adherence, and aging findings. • Provide regular updates to CTO and Cybersecurity leadership. • Identify operational weaknesses and lead long-term hardening initiatives that strengthen enterprise security posture. • Audit systems to identity security gaps as outlined in the security policy. Knowledge, Skills and Abilities: 1. Bachelor's degree, preferably in Computer Science, Cybersecurity, or related field. 2. 5–8 years of industry experience in cybersecurity, security engineering, vulnerability management, or IT operations. 3. Experience in the healthcare industry is highly desired. 4. Experience with infrastructure and cloud hardening, vulnerability management, patching, configuration management, and cloud security. 5. Strong understanding of vulnerability scanning tools, CVSS scoring, and common remediation workflows. 6. Ability to lead technical discussions and drive outcomes across cross-functional teams. 7. Familiarity with NIST CSF, CIS Controls, HIPAA, and PCI. 8. Experience coordinating remediation for red team and pen test findings. 9. Knowledge of cloud platforms (AWS, Azure, GCP) and cloud security. 10. Experience with ServiceNow, or similar ticketing systems. 11. Preferred certifications include CISM, CCSP, or relevant GIAC certifications are preferred. 12. Broad understanding of IT infrastructure, including networks, applications, databases, operating systems, backup, storage, and disaster recovery. 13. Familiarity with information management practices, system development life cycle management, IT services management, agile and lean methodologies, infrastructure and operations, and EA and ITIL frameworks. 14. Strong leadership skills with exceptional soft and interpersonal skills, including teamwork, facilitation, and negotiation. 15. Highly collaborative and supportive of business and of its ideals and strategies. 16. Highly innovative with aptitude for foresight, systems thinking and design thinking. 17. Excellent analytical, planning and organizational skills. 18. Excellent written, verbal, communication and presentation skills with the ability to articulate new ideas and concepts to technical and nontechnical audiences. 19. Knowledge of PowerShell and Internal Query Language scripting is required, Kusto Query Language is a plus. 20. Knowledge of and Microsoft Defender management is required, knowledge of Sentinel is a plus. Skills Vulnerability management, Vulnerability, Security, Vulnerability assessment, qualys, hippa, nist Top Skills Details Vulnerability management,Vulnerability,Security,Vulnerability assessment,qualys,hippa,nist Additional Skills & Qualifications Experience in the healthcare industry is highly desired. Experience Level Expert Level Job Type & LocationThis is a Contract to Hire position based out of Chicago, IL. Pay and BenefitsThe pay range for this position is $100.00 - $100.00/hr. Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to specific elections, plan, or program terms. If eligible, the benefits available for this temporary role may include the following: • Medical, dental & vision • Critical Illness, Accident, and Hospital • 401(k) Retirement Plan – Pre-tax and Roth post-tax contributions available • Life Insurance (Voluntary Life & AD&D for the employee and dependents) • Short and long-term disability • Health Spending Account (HSA) • Transportation benefits • Employee Assistance Program • Time Off/Leave (PTO, Vacation or Sick Leave) Workplace TypeThis is a fully remote position. Application DeadlineThis position is anticipated to close on Mar 18, 2026. h4>About TEKsystems: We're partners in transformation. We help clients activate ideas and solutions to take advantage of a new world of opportunity. We are a team of 80,000 strong, working with over 6,000 clients, including 80% of the Fortune 500, across North America, Europe and Asia. As an industry leader in Full-Stack Technology Services, Talent Services, and real-world application, we work with progressive leaders to drive change. That's the power of true partnership. TEKsystems is an Allegis Group company. The company is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law. About TEKsystems and TEKsystems Global Services We’re a leading provider of business and technology services. We accelerate business transformation for our customers. Our expertise in strategy, design, execution and operations unlocks business value through a range of solutions. We’re a team of 80,000 strong, working with over 6,000 customers, including 80% of the Fortune 500 across North America, Europe and Asia, who partner with us for our scale, full-stack capabilities and speed. We’re strategic thinkers, hands-on collaborators, helping customers capitalize on change and master the momentum of technology. We’re building tomorrow by delivering business outcomes and making positive impacts in our global communities. TEKsystems and TEKsystems Global Services are Allegis Group companies. Learn more at TEKsystems.com. The company is an equal opportunity employer and will consider all applications without regard to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.

• Design the ML lifecycle for computer vision models operating on edge platforms • Establish model versioning, validation, and deployment patterns suitable for disconnected tactical environments • Develop guardrails to ensure autonomy behavior remains predictable and auditable • Create architectures for collecting operational data and feeding it back into retraining pipelines • Build and maintain pipelines for model packaging, testing, and deployment to edge systems • Implement automated testing to ensure new models do not degrade performance • Develop repeatable processes so operators can update systems without ML expertise • Integrate data science outputs into fieldable, supportable software packages • Validate model performance against real operational data • Conduct regression testing to ensure updated models maintain or improve detection and tracking performance • Ensure traceability of which model versions were used during specific operations • Support field units in updating and maintaining onboard models • Troubleshoot issues related to model performance and deployment in operational environments • Continuously improve processes for safe model iteration and deployment • Create technical documentation for model lifecycle processes • Develop operator friendly guides for updating and validating onboard systems • Document model versioning, testing results, and deployment procedures

Stord is The Consumer Experience Company, powering seamless checkout through delivery for today's leading brands. Stord is rapidly growing and is on track to double our revenue in the next 18 months. To meet and exceed this target, Stord is strategically scaling teams across the entire company, and seeking energetic experts to help us achieve our mission. By combining comprehensive commerce-enablement technology with high-volume fulfillment services, Stord provides brands a platform to compete with retail giants. Stord manages over $10 billion of commerce annually through its fulfillment, warehousing, transportation, and operator-built software suite including OMS, Pre- and Post-Purchase, and WMS platforms. Stord is leveling the playing field for all brands to deliver the best consumer experience at scale. With Stord, brands can increase cart conversion, improve unit economics, and drive sustained customer loyalty. Stord’s end-to-end commerce solutions combine best-in-class omnichannel fulfillment and shipping with leading technology to ensure fast shipping, reliable delivery promises, easy access to more channels, and improved margins on every order. Hundreds of leading DTC and B2B companies like AG1, True Classic, Native, Seed Health, quip, goodr, Sundays for Dogs, and more trust Stord to deliver industry-leading consumer experiences on every order. Stord is headquartered in Atlanta with facilities across the United States, Canada, and Europe. Stord is backed by top-tier investors including Kleiner Perkins, Franklin Templeton, Founders Fund, Strike Capital, Baillie Gifford, and Salesforce Ventures. About the Role: Stord is building ML capabilities that directly power our cloud-based supply chain platform, which handles over $10B in commerce annually. You'll work alongside a Senior Data Scientist to own the full ML lifecycle — from model design and training through production deployment and ongoing improvement. This is a high-impact, hands-on role on a small team where your work will directly influence how millions of shipments are planned, routed, and fulfilled. As a Machine Learning Engineer at Stord, you will own the end-to-end delivery of ML features — designing and training models, deploying them to production, and ensuring they perform reliably at scale. You'll inherit an existing production model and expand our ML capabilities across core logistics use cases including delivery time estimation, demand forecasting, and operational intelligence. This is a rare opportunity to shape ML practices from the ground up on a small, ambitious team with direct visibility into customer impact. What You'll Do Model Development - Design, train, and evaluate ML models for logistics use cases: delivery time estimation, demand forecasting, capacity planning, anomaly detection - Improve and iterate on existing production models using performance data and customer feedback - Run structured experiments to validate model improvements before promotion to production - Define evaluation frameworks and success metrics in collaboration with the Data Scientist and product teams Productionization & Deployment - Own the full path from trained model to production API — wrapping, deploying, versioning, and monitoring - Build and maintain inference APIs serving predictions at scale (<100ms latency targets) - Deploy and manage models on GCP Vertex AI - Implement A/B testing and rollback strategies for safe model promotion Data Pipelines & Feature Engineering - Build real-time and batch feature pipelines from Postgres/AlloyDB sources - Design feature stores serving both training and inference - Implement data validation and quality monitoring to catch drift before it affects customers Infrastructure & Reliability - Develop CI/CD pipelines for model deployment - Monitor model and pipeline health; own incident response for ML systems - Optimize inference costs across GCP and Cloudflare infrastructure Collaboration - Partner with the Data Scientist on experiment design and feature strategy - Work with platform engineers to integrate ML outputs into core product services - Communicate model behavior, limitations, and tradeoffs clearly to non-ML engineers and product stakeholders What You'll Need Required - 4+ years of ML engineering experience, with models shipped to production - Strong Python — training pipelines, model evaluation, production code (not just notebooks) - Experience with cloud ML platforms, preferably GCP Vertex AI - Data engineering fundamentals: ETL/ELT, streaming data, SQL at scale - TypeScript or Elixir experience, or demonstrated ability to build APIs in unfamiliar languages - Familiarity with logistics, e-commerce, fulfillment, or supply chain domains — you understand what on-time delivery, carrier selection, and warehouse throughput actually mean operationally Bonus - Kafka or streaming pipeline experience - Feature store experience (Feast, Tecton, or equivalent) - Hands-on with Kubernetes or container orchestration - Cloudflare Workers or edge inference experience - Experience improving existing production models, not just building greenfield