Resilinc

• Work closely with Senior Software Engineers, Tech Leads, Product Managers, and Architects to build, maintain, and enhance backend services and distributed data pipelines that power our multi-tenant B2B SaaS platform. • Contribute to building scalable backend platforms, solve complex engineering challenges, and continuously grow your expertise in distributed systems, data engineering, and cloud technologies.

• Design, implement, and manage scalable and highly available systems on Azure Cloud • Monitor system performance, troubleshoot issues, and ensure uptime and reliability • Manage and optimize Kubernetes clusters and containerized workloads (Docker) • Build and maintain robust CI/CD pipelines using GitHub Actions and related tools • Implement infrastructure as code and deployment automation using Helm Charts • Work with distributed systems such as Kafka, Redis, PostgreSQL, Hadoop/HDFS • Configure and manage Cloudflare for performance, security, and traffic routing • Set up monitoring, alerting, and observability using tools like Grafana • Collaborate with development teams to improve system reliability and deployment practices • Perform root cause analysis (RCA) and implement preventive measures • Ensure security best practices and compliance across infrastructure

• Partner directly with enterprise customers and business stakeholders to understand data challenges, define requirements, and deliver scalable solutions. • Own technical delivery for customer engagements from discovery and solution design through implementation and adoption. • Help customers maximize the value of Resilinc’s data and analytics capabilities through effective solution design and execution. • Present technical architectures, implementation plans, and business outcomes to both technical and executive audiences. • Design, build, and maintain scalable batch and real-time data pipelines and data products using Databricks, ClickHouse, Python, and distributed data processing frameworks. • Architect modern cloud-native data platforms leveraging Databricks, Delta Lake, Azure Data Services, and distributed processing technologies. • Develop robust ETL/ELT frameworks, data models, and data quality monitoring processes. • Optimize data storage, query performance, scalability, reliability, and cost efficiency across large-scale datasets. • Build and maintain data products that enable analytics, reporting, operational workflows, and AI/ML applications. • Collaborate closely with Product, Engineering, Solutions Consulting, and Customer Success teams to deliver customer outcomes. • Drive best practices in data governance, security, observability, reliability, and operational excellence. • Contribute to architecture decisions, technical design reviews, and platform evolution. • Mentor junior engineers and contribute to engineering best practices.

• Partner directly with enterprise customers to understand business challenges, identify AI opportunities, and define solution requirements. • Lead customer engagements from discovery and solution design through deployment, adoption, and measurable business impact. • Serve as a trusted technical advisor, helping customers successfully implement and scale AI-driven solutions. • Present technical concepts, solution recommendations, and business outcomes to audiences ranging from practitioners to executive leadership. • Design, build, and deploy scalable machine learning, Generative AI, and agentic AI solutions for real-world supply chain challenges. • Develop production-ready ML pipelines, AI applications, prototypes, and proof-of-concepts. • Evaluate emerging AI technologies and adapt state-of-the-art techniques to customer and product needs. • Ensure solutions balance technical excellence, scalability, and business value. • Collaborate closely with Product, Engineering, Solutions Consulting, Sales, and Customer Success teams to deliver customer outcomes. • Influence product strategy by bringing customer insights, implementation learnings, and industry trends back into the organization. • Mentor team members and contribute to best practices in AI solution development and deployment.

• Handle all requests coming into the Customer Support team through email, chats, phone and workflow tool in line with the Service Level Agreements • Own and resolve L3-level technical issues, including debugging, root cause analysis, and minor code fixes (Java, Python, .NET) • Record, track and document the Customer Support request problem-solving process, including all successful and unsuccessful decisions made, query used, and actions taken, through to the final resolution • Work closely with Engineering, Data, Product, and module owners to drive issue resolution and bug fixes • Drive AI Agent implementation and deployment, ensuring data readiness and resolving issues such as hallucination, fallback handling, and prompt misinterpretation • Troubleshoot and resolve REST API issues and analyze complex system logs to identify root causes • Be thorough & detail oriented in the analysis & evaluation of a request, methodical and be able to think of alternative routes to problem solving, so as to expedite problem resolution • Coordinate product testing during product upgrades and releases • Be results-driven and passionate about product, data & what you do day-to-day • Escalate problems (when required) to the appropriately experienced analyst, leader and internal teams • Help develop knowledge base articles for team & end users • Provide periodic report & present analysis on status, category, type & root cause of all requests received at Customer Support • Comfortable communicating with all levels of leadership from internal & external customers. Should have strong verbal & written communication skills • Effectively prioritize and execute tasks in a high-pressure environment. Consistently meet deadlines and work well within a team environment • Ensure compliance with service agreements and quality standards, while prioritizing effectively in a high-pressure environment

• Lead complex enterprise deployments from ambiguity to production • Partner directly with strategic customers and internal teams to understand business goals, technical constraints, data realities, delivery risks, and success metrics • Translate those inputs into a clear technical approach that accelerates deployment and value realization • Design, build, and deliver production-quality solutions required for complex deployments • Enable agentic AI deployments in real enterprise environments • Create solutions that are fast to deliver and safe to inherit • Reduce friction for high-complexity accounts • Turn customer-specific work into product and platform insight • Help define how forward-deployed engineering scales

• Deliver production-ready technical solutions for Resilinc’s most complex enterprise customers by turning ambiguous business, data, workflow, and AI deployment requirements into scalable, well-documented, supportable software and implementation patterns. • Lead complex enterprise deployments from ambiguity to production • Partner directly with strategic customers and internal teams to understand business goals, technical constraints, data realities, delivery risks, and success metrics. • Design, build, and deliver production-quality solutions required for complex deployments, including data ingestion and transformation utilities, ERP, Snowflake, Databricks, API, and external system integrations, workflow automations, and customer-specific data validation and enrichment tools. • Help customers deploy and operationalize Resilinc’s agentic capabilities across use cases such as disruption intelligence, tariff risk, forced labor compliance, supplier risk, multi-tier mapping, and event-driven workflows. • Establish strong engineering discipline for everything shipped, including clean code, testing, documentation, security considerations, logging and observability, deployment readiness, operational handoff, and supportability. • Work on the most demanding deployments where standard playbooks are not enough. • Identify repeated patterns across enterprise deployments and feed clear, actionable recommendations back to Product and Engineering.

• Work directly with enterprise customers to understand business workflows, data environments, and risk challenges • Design scalable AI-driven solutions aligned to customer objectives and platform capabilities • Lead implementation of data integrations (DFI/CDIF), workflows, and AI use cases • Configure and deploy solutions in customer environments with speed and accuracy • Own time-to-value from onboarding through initial outcomes • Build and deliver Proof of Concepts and MVPs that demonstrate measurable business impact • Serve as a technical expert across customers and internal teams • Train internal teams and customers on platform capabilities, best practices, and solution design • Partner with Product and Engineering to relay customer feedback and influence roadmap • Work closely with Implementation, Sales, and Customer teams to ensure successful adoption

• Drive growth by positioning our AI-driven supply chain platform as a mission-critical solution for global enterprises • Lead disciplined, value-based sales cycles from discovery through POV, negotiation, and close • Build and maintain trusted relationships with senior leaders, from VP-level stakeholders through C-suite executives responsible for supply chain continuity and risk • Develop and execute quarterly account strategies aligned to customer priorities, regulatory requirements, and long-term growth objectives • Maintain accurate pipeline visibility, opportunity updates, and revenue forecasts in Salesforce • Partner with Alliances and Partnerships teams to expand pipeline, accelerate deal velocity, and support multi-channel growth • Serve as a trusted advisor and subject matter expert in supply chain resilience, risk, and compliance within assigned verticals