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Lead Applied AI Site Reliability Engineer II Role Overview: As a Lead Applied AI Site Reliability Engineer II , you will actively engage in your engineering craft, taking a hands-on approach to the reliability, performance, and operational integrity of high-visibility products and platforms and the environments they run in. Your expertise will be pivotal in keeping production safe, performant, and cost-effective, while driving tangible value for Deloitte's engineering investments. You will leverage your extensive engineering craftsmanship and advanced proficiency across cloud platform engineering, observability, and performance and reliability engineering-together with applied AI fluency that lets you reliably operate AI and agentic workloads alongside the rest of the portfolio-consistently demonstrating your exemplary track record in operating high-quality, resilient systems at scale. The ideal candidate will be a role-model leader and mentor, collaborating with cross-functional teams to set production standards, safeguard environments, and admit systems into production with confidence. Key Responsibilities: Outcome-Driven Accountability: Embrace and drive a culture of accountability for reliability, performance, and cost outcomes, measured in service-level objectives and error budgets, not raw uptime. Operate the products, platforms, and environments you support to meet their SLOs within budget, and track incident trends and toil to prioritize the work that most improves reliability-ensuring high-quality, lean operational designs that keep production safe and resilient. Technical Leadership and Advocacy: Serve as the technical advocate for production reliability and operability, ensuring systems are admissible, performant, safe to run, and able to degrade gracefully when failure occurs. Set production standards, lead the design of observability, performance and resilience testing, and operational tooling, and own the admission of systems into production-gating release on error budgets and automated reliability checks, and owning the readiness verification, environment integrity, and operational support that follow. Engineering Craftsmanship: Maintain accountability for the operational integrity of production and pre-production environments, and for the production standards that systems are admitted against. Own SLOs and error budgets; build and operate production observability-codified, version-controlled dashboards and SLO-driven, actionable alerting that detects before impact, plus the feedback loop into engineering; run performance, ambient-noise, and chaos testing to verify readiness; and guard environments against drift. Stay hands-on, self-driven, and continuously learn new approaches, languages, and frameworks-operating as an infrastructure-focused engineer, not a tool operator. Create technical specifications, runbooks, and shared playbooks; lead blameless postmortems that turn incidents into learning and systemic fixes; write high-quality, supportable automation; and review the work of other engineers, mentoring them, to ensure all reliability KPIs (availability, performance, and cost) are met or exceeded. Demonstrate collaborative skills to work effectively with diverse teams. Customer-Centric Engineering: Develop lean operational solutions through rapid, inexpensive experimentation to meet the reliability needs of the engineering teams and the business. Engage with those teams before, during, and after delivery, co-defining service-level objectives and operational readiness so the right safeguards are in place at the right time, without becoming a bottleneck to delivery. Incremental and Iterative Delivery: Adopt a mindset that favors action and evidence over extensive planning. Utilize a leaning-forward approach to navigate complexity and uncertainty, hardening reliability through incremental, measurable improvements-progressive resilience testing and SLO refinement-rather than big-bang interventions, and keeping operations supportable and maintainable. Cross-Functional Collaboration and Integration: Work collaboratively with empowered, cross-functional partners: engineering, platform engineering, security and risk, data governance, and engineering leadership and architecture. Set production standards and integrate their constraints so that the reliable, performant, and compliant path is the operative path. Co-define service-level objectives with the teams you support, verify readiness, and own the admission decision into production-holding the segregation-of-duties line as a dedicated, embedded function while partnering with security and risk on the control objectives you enforce. Foster a collaborative environment that enhances team synergy and innovation. Advanced Technical Proficiency: Possess deep expertise in site reliability and modern production engineering-cloud platform ownership, observability (metrics, tracing, logging), performance and capacity engineering, chaos engineering, and cloud/AI cost engineering-together with applied AI fluency to operate AI and agentic workloads reliably, including AI and Agentic SSDLC, delivering production operations with full automation from discovery to production to operations and all quality checks through the SSDLC lifecycle. Be a role model, leveraging these techniques to optimize reliability, performance, and operational delivery. Demonstrate strong understanding of the full lifecycle of platform and product development, focusing on continuous improvement and learning. Domain Expertise: Quickly acquire domain knowledge of the products and platforms you operate-and, where they are AI-infused, their distinct production failure modes such as drift, train/serve skew, latency and output variance, and token/GPU cost anomalies. Translate reliability needs, reference architectures, and operational requirements into service-level objectives, runbooks, and production tooling. Be a valuable, flexible, and dedicated team member, supportive of teammates, and focused on quality and tech debt payoff. Effective Communication and Influence: Exhibit exceptional communication skills, capable of articulating complex technical concepts clearly and compellingly. Inspire and influence teammates and product teams through well-structured arguments and trade-offs supported by evidence. Create coherent narratives that align technical solutions with business objectives. Engagement and Collaborative Co-Creation: Engage and collaborate with product engineering teams at all organizational levels, including customers as needed. Build and maintain constructive relationships, fostering a culture of co-creation and shared momentum towards achieving product goals. Align diverse perspectives and drive consensus to create feasible solutions. The team: US Deloitte Technology Product Engineering has modernized software and product delivery, creating a scalable, cost-effective model that focuses on value/outcomes that leverages a progressive and responsive talent structure. As Deloitte's primary internal development team, Product Engineering delivers innovative digital solutions to businesses, service lines, and internal operations with proven bottom-line results and outcomes. It helps power Deloitte's success. It is the engine that drives Deloitte, serving many of the world's largest, most respected companies. We develop and deploy cutting-edge internal and go-to-market solutions that help Deloitte operate effectively and lead in the market. Our reputation is built on a tradition of delivering with excellence. The successful candidate will possess: Excellent interpersonal and organizational skills, with the ability to handle diverse situations, complex projects, and changing priorities, behaving with passion, empathy, and care. Required Qualifications: A bachelor's degree in computer science, software engineering, data science, machine learning, or related discipline. Experience is the most relevant f
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.