• Site Reliability Engineers are responsible for helping balance development velocity against customer-centric stability of systems through the use of SRE best practices and the creation of new processes with automation. • The Senior Site Reliability Engineer is responsible for creating and leading projects around how services and other workflows should be measured as well as participating in the observability of production systems and services through day to day operational responsibilities with the intention to gain that wisdom of production to then decide what toil should be automated next. • The Senior Site Reliability Engineer is expected to operate with a DevOps mindset at the convergence point of Cloud Operations, Engineering, and Technical Support within the framework of the Agile process. • Identify improvements in both the platform and processes by implementing established SRE concepts with the goal of improving product and system reliability. • Proactively engage and collaborate with other individuals and teams as issues arise by serving as an escalation point of customer issues to ensure successful outcomes. • Perform root cause analysis for customer impacting issues and be able to clearly document the solution and advise others from the results of those findings. • Create technical documentation based upon new technology proof of concepts, project work, root cause issue analysis, identification of alerting patterns, and proactively sharing this knowledge with other teams as part of the Continuous Improvement Model. • Participate in team ceremonies to identify and refine potential work, communicate findings, and drive opportunities to collaborate. • Assign and communicate the business value and benefit hypothesis of new projects, initiatives, and strategies while being able to break down the technical work require to achieve a successful outcome. • Lead cross-team and cross-department technical collaboration in critical customer escalations. • Advise stakeholders and senior leadership on critical customer escalations. • Occasionally provide off hours support for deployments and customer escalations.

• Own and evolve the edge proxy platform: Maintain, upgrade, and extend a high-performance reverse proxy — including maintaining the proxy binary and its configuration tooling, writing Go and Python automation, managing the full container image lifecycle on hardened Linux base images, and working across the broader edge layer, including CDN, WAF, and traffic management capabilities. • Build and maintain cloud infrastructure as code: Design and implement Terraform/Terragrunt modules and live environment configurations managing EKS clusters, load balancers, IAM roles, VPC networking, ECR registries, and supporting AWS services across multiple regions including GovCloud. • Operate Kubernetes clusters at scale: Manage multi-region, multi-cluster EKS deployments via FluxCD GitOps workflows and Helm charts, including node AMI rotation, add-on lifecycle management, and horizontal pod autoscaling. • Build and own CI/CD pipelines: Design, maintain, and improve shared GitLab CI/CD pipeline templates used across all team repositories; build and operate alternative pipeline workflows for isolated government cloud environments. • Automate operational toil: Build and maintain tooling for tasks such as container image patching, EKS AMI rotation, air-gapped ECR image sync to GovCloud, and automated MR creation for monthly version-bump patching cycles. • Manage observability and on-call: Provision and maintain Datadog SLOs, monitors, and dashboards via Terraform; participate in the team's on-call rotation responding to edge proxy incidents across production and GovCloud environments. • Support FedRAMP/GovCloud operations: Operate the GovCloud environment with its unique constraints — air-gapped image distribution, infrastructure automation in isolated networks, and alert management with compliance-aware data handling. • Evaluate and adopt internal developer tooling: Research, prototype, and drive the adoption of internal tools that improve engineering productivity across the company — including developer portals, platform self-service capabilities, and other tooling that raises the bar for the developer experience at Smartsheet. • Mentor and collaborate: Share knowledge across the team through code reviews, architecture discussions, and runbook authorship; foster a culture of engineering excellence and operational rigour. • Strategically apply AI tools: Strategically apply and champion AI tools within your team's domain to improve project execution, infrastructure design, quality, and debugging, leading adoption of AI best practices.

Role Description The AI Solutions Engineer is a hands-on technical role focused on building, deploying, and supporting AI-driven solutions, including AI and Digital Employee offerings, that address real-world client and internal business needs. Operating at the intersection of emerging AI technologies and practical implementation, this role is responsible for executing on defined solution architectures, developing proof-of-concept and production-ready systems, and integrating AI capabilities into scalable, supportable environments. This individual works closely with Innovation leadership, AI Architects, engineering, and customer-facing teams to bring AI solutions to life. Responsibilities include: - Develop and support AI-based managed services and Digital Employee offerings from prototype through production deployment. - Engineer agentic systems with memory, tools, and orchestration. - Help define the future Digital Workforce platform. - Build and implement AI-driven solutions with a focus on LLMs, generative AI, AI agents, and automation platforms. - Execute on defined solution architectures by developing, integrating, and deploying AI systems. - Design and deliver proof-of-concept implementations to validate AI use cases. - Collaborate with AI Architects, Innovation, engineering, and operations teams. - Integrate AI solutions into client environments. - Ensure AI solutions align with client business objectives and governance requirements. - Support client engagements including technical discussions and ongoing optimization. - Translate complex AI concepts into clear, practical guidance. - Contribute to documentation, enablement materials, and internal training. - Continuously monitor emerging tools, frameworks, and best practices. - Participate in cross-functional planning sessions to align AI initiatives. Qualifications - Bachelor’s degree in Computer Science, Information Technology, Data Science, or a closely related discipline. - 3–6+ years of experience in software engineering, solution implementation, automation, or related technical roles. - Hands-on experience building or implementing artificial intelligence, machine learning, generative AI, or large language model (LLM)-based systems. - Experience developing and integrating APIs, automation workflows, or distributed systems in production environments. - Relevant certifications related to cloud platforms, AI/ML, or software engineering are preferred but not required. Requirements - Hands-on experience building and deploying AI solutions, including large language models (LLMs), agent-based systems, and automation workflows. - Proficiency in software development and integration (e.g., Python, APIs, SDKs, or equivalent modern development tools). - Experience working with cloud platforms and modern application architectures (cloud-native or hybrid environments). - Ability to implement and integrate AI solutions into business processes and existing technology ecosystems. - Strong problem-solving skills with the ability to move from concept to working solution quickly. - Effective communication skills, with the ability to explain technical concepts to both technical and non-technical audiences. - Proven ability to collaborate across engineering, innovation, sales, and delivery teams. - Demonstrated curiosity and commitment to continuous learning in a rapidly evolving AI landscape. - Ability to balance rapid experimentation with building stable, supportable solutions. Benefits - Pay Range: $109,855 - $175,768 annually. - The pay range listed for this position is based on candidate's skill level, experience, relevant licenses, and educational background. - For detailed information about our benefits, please visit our careers page at www.marconet.com/careers . Location This is a remote-eligible position; however, Marco Technologies requires employees to reside within one of the following states: DE, FL, IA, IL, IN, KY, MD, MI, MN, MO, ME, NE, ND, NJ, PA, RI, SD, TX, WI.

Role Description The DevOps / SRE Engineer owns the operational substrate of an AI-native retail decisioning platform — infrastructure, CI / CD, observability, cost meter, and incident response for a system that runs production agents taking real business actions. The role builds on the enterprise Terraform standard, CI / CD spine, and FinOps tagging policy rather than reinventing parallel infrastructure. Remote candidates outside of Thailand are welcome to apply. - Adopt the enterprise Terraform standard and module library for all platform infrastructure; author platform-specific modules where needed (agent runtime, vector DB, knowledge graph); run drift detection weekly. - Build platform-specific CI / CD pipelines on the enterprise spine — service deploys, agent deploys, eval-gate enforcement; integrate eval gates so no agent reaches production without eval pass. - Operate rollback orchestration with sub-15-minute recovery; quarterly game days. - Own the platform observability stack — OpenTelemetry, Langfuse for LLM traces, custom dashboards for per-agent cost. - Implement the per-agent cost meter end-to-end — token counts, vector queries, model inference, downstream LLM Gateway costs; surface cost data to the enterprise GenAI cost dashboard. - Stand up the platform on-call rotation; author runbooks for every production agent and service; lead incident response with measurable corrective actions. - Implement platform cost-tagging policy consistent with the enterprise standard (team, domain, environment, project, agent, suite, persona); report monthly to Cost Review. - Drive cost optimisation — right-sizing, caching, model routing decisions, reserved compute. Qualifications - Bachelor's or Master's degree in Computer Science, Engineering, or a related discipline. - 5+ years SRE / DevOps with production ownership. - Terraform at scale — modules, state, drift, environment promotion. - CI / CD for data + ML / AI services (GitLab CI / CD or comparable). - Cloud platform (Azure preferred; AWS / GCP transferable). - Observability — OpenTelemetry, Langfuse (or comparable LLM traces), custom dashboards. - FinOps — tagging policies, attribution, optimisation. - Incident response — on-call, post-mortems, runbook authorship. Preferred Qualifications - AI / agent platform SRE experience; cost-meter / chargeback systems built or operated. - Multi-cloud production experience; open-source contributions to IaC / observability tooling. - AI / ML / agent system observability instrumentation (LLM cost, agent cost, eval scores). - Vendor certifications such as HashiCorp Terraform Associate / Professional, Azure Solutions Architect Associate, or Databricks Data Engineer Professional.