Lead Engineer - AIOps

Job Responsibilities

• Leads the design and architecture of software and platform solutions by deeply understanding business needs; translating requirements into scalable, maintainable system designs across the full software development lifecycle.
• Evaluates operational and technical feasibility of proposed solutions; provides architectural guidance and sign-off on solution approaches across the team.
• Owns documentation standards — establishing and enforcing best practices for flowcharts, architecture diagrams, code comments, and technical specifications across the team.
• Defines system specifications, standards, and programming guidelines; ensures solutions are built to those standards consistently across delivery workstreams.
• Leads the design and operationalization of AIOps platforms that automate event correlation, anomaly detection, and incident remediation across client environments; accountable for platform reliability and outcomes.
• Defines the strategy for integrating AI/ML models into operational workflows — including predictive alerting, root-cause analysis, and capacity forecasting — and oversees implementation by the engineering team.
• Architects RAG pipelines and Agentic AI workflows; makes key technology and design decisions, and guides senior and junior engineers through implementation.
• Owns and enforces the AI Development Lifecycle (AIDLC) across the team — setting standards for problem framing, data readiness, model selection, integration, testing, deployment, monitoring, and responsible AI practices.
• Drives AI-accelerated development practices across the team — establishing norms for the usage of tools such as Claude, ChatGPT, Cursor, and GitHub Copilot to improve engineering velocity, code quality, and delivery outcomes.
• Owns CI/CD pipeline architecture and infrastructure-as-code standards; reviews and approves pipeline designs before production deployment.
• Oversees containerized workload strategy on Kubernetes (EKS, AKS, or on-prem) — including cluster design, scaling policies, and operational runbooks.
• Leads observability strategy definition — establishing metrics, logs, and traces standards organisation-wide and driving adoption of AI-driven insights into monitoring dashboards.• Drives client engagement across the full lifecycle — leading discovery workshops, owning solution design, presenting architecture proposals, and participating in pre-sales activities and client governance reviews.
• Identifies and pursues automation and process improvement opportunities; conducts systems analysis and recommends changes in policies, tooling, and engineering procedures at both team and client levels.
• Updates job knowledge by staying current with state-of-the-art development tools, programming techniques, and emerging technologies; actively participates in industry communities, publications, and professional networks.
• Mentors and develops engineers across the team — providing technical guidance, career coaching, code review, and educational opportunities; accountable for raising the overall capability of the engineering team.
• Your future at Presidio Joining Presidio means stepping into a culture of trailblazers—thinkers, builders, and collaborators—who push the boundaries of what’s possible. With our expertise in AI-driven analytics, cloud solutions, cybersecurity, and next-gen infrastructure, we enable businesses to stay ahead in an ever-evolving digital world.
• Here, your impact is real. Whether you're harnessing the power of Generative AI, architecting resilient digital ecosystems, or driving data-driven transformation, you’ll be part of a team that is shaping the future.