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.

• Design, build, and operate production‑grade Kubernetes clusters on bare metal and cloud • Industrialize, automate, improve observability & monitoring • Continue to create a culture of service delivery excellence • Participate in on‑call rotation, incident management, and post‑incident reviews • Design and drive projects around DevSecOps practices in the company

The best way to look at this role is you would have the main responsibility to own our Linux systems and the responsibility for deployment and the support of team that will handle the maintenance and monitoring of applications on them. This will include custom and other more standard open source and proprietary applications. Our favorite candidate will be able to support at least basic needs for Window DevOps and more. **RESPONSIBILITIES:** - Plan/Design, Build and define the monitoring of our Linux and Windows applications and the systems that run them. - Implement client-requested integrations. - Design/Plan and support team in deploying updates and fixes - Conduct root cause analysis if issues - Investigate and resolve technical issues and create/provide resources to team to prevent. - Develop scripts to automate processes and updates - Provide technical support and design procedures for system troubleshooting and maintenance.