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About This Role
Applied AI Site Reliability Engineer III
Role Overview: As an Applied AI Site Reliability Engineer III , 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 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 strong track record in operating high\-quality, resilient systems at scale. The ideal candidate will be a dependable team player, collaborating with cross\-functional teams to uphold 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. Uphold 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 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. Uphold 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 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. Strive to 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 factor.
- 5\+ years of software engineering and site reliability engineering experience operating large\-scale, distributed, cloud\-native systems in production, with experience in most of the following: Python, Go, Bash, Java, C\#/.NET, SQL/NoSQL, Kubernetes, Terraform, ArgoCD, as well as CI/CD and observability stacks.
- 3\+ years of experience in site reliability or production engineering for large\-scale systems\-defining and owning SLIs, SLOs, and SLAs; error budgets; incident command and on\-call; building and operating production observability (metrics, tracing, logging\-e.g., OpenTelemetry, Prometheus, Grafana, Datadog, Dynatrace, Amazon CloudWatch, Azure Monitor, Google Cloud Operations, SolarWinds, Splunk); environment integrity and drift prevention across pre\-production and production; and segregation\-of\-duties controls (least\-privilege/RBAC, deploy approvals, secrets management) in partnership with security and risk.
- 3\+ years of experience with cloud\-native engineering and cloud platform ownership on any of the cloud hyperscalers such as Azure, AWS, or GCP\-including their AI/ML services such as Azure OpenAI, AWS Bedrock, or Vertex AI\-plus container orchestration (Kubernetes, Docker), infrastructure\-as\-code, networking, and multi\-environment management.
- Prior experience operating AI/ML and agentic workloads in production\-their reliability failure modes (drift, train/serve skew, output variance), MLOps/LLMOps, and the AI control plane (model/LLM gateway, guardrails) from the operability and performance side.
- Prior experience with load and performance testing under simulated production traffic (e.g., LoadRunner, k6, or JMeter), chaos engineering (e.g., Azure Chaos Studio, AWS Fault Injector), capacity planning, autoscaling, and cloud/AI cost engineering (FinOps tooling/dashboards, including GPU/inference and token cost attribution).
- Prior software engineering experience with the understanding of Business Context Diagrams (BCD), sequence/activity/state/entity relationship/data flow diagrams, OOP/OOD, data structures, algorithms, and code instrumentations, and AI\-augmented spec\-driven development.
- Prior experience using methodologies \& tools such as XP, Lean, DevSecOps, SRE, ADO, GitHub, SonarQube, MLflow, and agentic AI frameworks (e.g. LangFuse, LangSmith, or equivalent multi\-agent orchestration tools) etc. to operate high\-quality, resilient platforms and products at scale.
- Candidates must be located within a commutable distance to one of the select locations available for this role
- Ability to work in your local office at a minimum of 3 days per week
Other:
- Ability to travel 10%, on average, based on the work you do and products you build.
- Limited immigration sponsorship may be available.
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $102500 to $210600\.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
EA\_ExpHire
Salary Context
This $102K-$210K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Deloitte, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($156K) sits 27% below the category median. Disclosed range: $102K to $210K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Deloitte AI Hiring
Deloitte has 59 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Engineer, Research Engineer. Positions span Rosslyn, VA, US, Baltimore, MD, US, Morristown, NJ, US. Compensation range: $140K - $379K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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