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About This Role
Who We Are
We're the Accenture Google Business Group Mid\-Market team, a specialized group of engineers, architects, and builders delivering tailored Google Cloud solutions for mid\-market, high\-growth, and fast\-paced enterprises. We partner with dynamic organizations to connect them with the right combination of Accenture solutions and Google Cloud products to accelerate business transformation and solve real\-world problems, quickly. We're expanding our team to meet the unique, rapid execution needs of the mid\-market segment.
The Domain
Data and AI are no longer back\-office functions — they're the engine of every modern business decision. And with the arrival of agentic AI, we're entering a new era: intelligent agents that don't just analyze data but act on it — automating complex workflows, making autonomous decisions, and transforming how companies operate. Google Cloud is leading this shift with the Gemini Enterprise Agent Platform, Vertex AI, and the most powerful data stack in the industry. Our mid\-market clients need lean, agile leaders who can unlock massive amounts of untapped data and rapidly turn it into a distinct competitive advantage.
The Role
You're the person the client trusts to make it happen.
As a Technical Delivery Lead, you own delivery from initial solution shaping through go\-live. You design the execution approach for data and AI engagements, lead the implementation, and ensure the final product fundamentally upgrades how the client leverages data and intelligent automation. You're supported by a dedicated offshore engineering team — you set the strategic direction, and they build alongside you. But you're the single point of accountability for what ships.
This role is for an execution\-focused leader who takes full ownership of their work. You care deeply about delivery quality, data architecture outcomes, agent design, and speed\-to\-value for the business. You hold yourself to a higher standard than anyone else would. You'll work directly with client stakeholders — immersing yourself in their environment, understanding their fast\-evolving resource constraints, and delivering outcomes that matter in weeks, not quarters. You'll have the autonomy to shape how they experience the full power of Google Cloud's data and AI stack, from first conversation to go\-live.
What You'll Do
- Own the delivery. End\-to\-end. Scope, timeline, team structure, quality, outcome. You're the single point of accountability the client relies on.
- Design how it gets delivered. Shape the delivery approach for complex data and AI engagements — define the delivery plan, phase the work, structure the team, and manage dependencies across data engineering, analytics, and AI workstreams. Partner with solution engineers who own the technical architecture; you own how it gets executed and shipped.
- Provide technical advisory across the engagement — Guide our mid\-market clients and teams on our offerings, technical implementation, and the underlying Google Cloud services, with a deep understanding of each client's existing environment and architecture.
- Be the client's trusted delivery partner. You're the face of the engagement. Lead status reviews, manage expectations, navigate trade\-offs in real time, and ensure the client always knows where things stand — especially when the work involves iterative AI development where scope evolves.
- Get solutions into clients' hands fast. Pick up where the deal ends and make it real. Stand up the delivery, mobilize the team, and drive execution from day one — delivering proven capabilities in weeks, not quarters.
- Lead across borders. Coordinate onshore delivery leadership with offshore engineering teams. Set priorities, define workstreams, remove blockers, and keep the entire delivery machine moving at pace.
- Drive quality and outcomes. Establish delivery governance, define checkpoints, track progress against commitments, and ensure every engagement delivers measurable client value — from data platform readiness to AI models in production.
Technical Skills \& Domain Expertise
- Data Platforms \& Engineering: Advanced proficiency in BigQuery, Dataflow, Pub/Sub, Cloud SQL, Spanner, and Bigtable for data migration, modernization, pipeline design, data quality governance, and lakehouse architecture patterns.
- Analytics \& Business Intelligence: Hands\-on experience executing Looker deployments, connected sheets integrations, real\-time operational dashboards, and semantic modeling.
- Artificial Intelligence \& Machine Learning: Core understanding of Vertex AI, Gemini models, AutoML, custom model training, and building production\-grade MLOps deployment pipelines.
- Agentic \& Generative AI Patterns: Working knowledge of modern agent orchestration patterns, including tool use, grounding mechanisms, Retrieval\-Augmented Generation (RAG), Gemini Enterprise Agent Platform, Agent Development Kit (ADK), A2A protocol, and Model Context Protocol (MCP) — specifically for deploying intelligent agents that automate complex business processes.
What Sets You Apart
- Google Cloud Certifications: Active Google Cloud Professional certifications (specifically Professional Data Engineer, Professional Machine Learning Engineer, or Professional Cloud Architect).
- Advanced Agent Deployments: Hands\-on experience with the Gemini Enterprise Agent Platform, ADK, A2A, or MCP.
- Production AI Experience: Experience building production\-grade ML/AI systems — not just prototypes.
- Cross\-Functional Ecosystem Exposure: Secondary familiarity with adjacent Google domains to maximize mid\-market account impact, including:
+ Marketing \& Personalization: Customer 360, CDP setups, and predictive segmentation using BigQuery \+ Vertex AI.
+ Customer Engagement: GECX and CCAI platforms for AI\-powered customer interactions built directly on the data layer.
+ Infrastructure Foundations: GKE, Terraform, and progressive CI/CD data pipeline deployments.
+ Cybersecurity: Google Security Operations and Agentic Defense architectures for securing data assets.
+ Workspace Productive Tech: Gemini Enterprise for Workspace internal collaboration workflows.
- Transformation Track Record: A documented track record delivering data platform modernizations and AI solutions at pace across multiple concurrent clients.
- Data Governance Depth: Deep expertise in multi\-pillar data governance, enterprise migration strategy, and lakehouse architecture on GCP.
Travel may be required for this role. The amount of travel will vary from 25% to 100% depending on business need and client requirements.
What You'll Need:
- Minimum 7 years in hands\-on, client\-facing technology roles — you've built and delivered, not just managed.
- Minimum 5 years architecting and delivering on Google Cloud Platform, with deep expertise in data engineering, analytics, AI/ML, and modern data platforms.
- Independently owned client engagements from technical design through go\-live.
- Deep hands\-on proficiency with BigQuery, Vertex AI, Dataflow, and data pipeline architecture — you can design a data platform, build a model, and troubleshoot a broken pipeline.
- Working knowledge of agentic AI patterns — agent orchestration, tool use, grounding, retrieval\-augmented generation.
- Strong in both technical leadership and delivery management — architecture, scope, risk, stakeholders.
- Experience with distributed global teams and modern engineering practices.
- Clear communicator — whiteboarding with engineers or presenting to a C\-suite.
- Bachelor's degree or equivalent (minimum 12 years) work experience. (If Associate’s Degree, must have minimum 6 years work experience)
Preferred Skills:
- Bachelor's in CS, Engineering, or related field — or 12 years equivalent experience.
- Google Cloud Professional certifications (Data Engineer, ML Engineer, Cloud Architect)
- Hands\-on experience with Gemini Enterprise Agent Platform, ADK, A2A, or MCP
- Experience building production ML/AI systems — not just prototypes
- Track record delivering data platform modernizations and AI solutions at pace across multiple clients
- Deep expertise in data governance, migration strategy, and lakehouse architecture on GCP
Domain Expertise
Primary — Data \& Agentic AI
Domain Area
Skills \& Technologies
Data Platforms
BigQuery, Dataflow, Pub/Sub, Cloud SQL, Spanner, Bigtable — data migration, modernization, lakehouse architecture
Analytics \& BI
Looker, connected sheets, real\-time dashboards, semantic modeling
AI / ML
Vertex AI, Gemini models, AutoML, custom model training, MLOps pipelines
Agentic \& Generative AI
Gemini Enterprise Agent Platform, Agent Development Kit (ADK), RAG, A2A protocol, MCP — intelligent agents that automate complex business processes
Data Engineering
Pipeline design, data quality, governance, migration at scale
Also Valuable
Domain Area
Skills \& Technologies
Marketing \& Personalization
Customer 360, CDP, predictive segmentation using BigQuery \+ Vertex AI
Customer Engagement
GECX, CCAI — AI\-powered customer interactions built on the data layer
Infrastructure
GKE, Terraform, CI/CD — foundational for data platform deployments
Cybersecurity
Google Security Operations, Agentic Defense — securing data assets
Workspace
Gemini Enterprise for Workspace — AI\-powered productivity and collaboration
What We Believe
Inclusion and diversity are fundamental to our culture. Our rich diversity makes us more innovative and creative, which helps us better serve our clients and our communities. Sustainability is one of our greatest responsibilities, embedded into everything we do.
Accenture is committed to providing equal employment opportunities for persons with disabilities. Please let us know if you require reasonable accommodation during the recruitment process
Compensation at Accenture varies depending on a wide array of factors, which may include but are not limited to the specific office location, role, skill set, and level of experience. As required by local law, Accenture provides a reasonable range of compensation for roles that may be hired as set forth below.
We anticipate this job posting will be posted until 08/23/2026\.
Accenture offers a market competitive suite of benefits including medical, dental, vision, life, and long\-term disability coverage, a 401(k) plan, bonus opportunities, paid holidays, and paid time off. See more information on our benefits here:
U.S. Employee Benefits \| Accenture
Role Location Annual Salary Range
California $94,400 to $266,300
Cleveland $87,400 to $213,000
Colorado $94,400 to $230,000
District of Columbia $100,500 to $245,000
Illinois $87,400 to $230,000
Maine $80,400 to $196,000
Maryland $94,400 to $230,000
Massachusetts $94,400 to $245,000
Minnesota $94,400 to $230,000
New York $87,400 to $266,300
New Jersey $100,500 to $266,300
Virginia $87,400 to $245,000
Washington $100,500 to $245,000
Requesting an Accommodation
Accenture is committed to providing equal employment opportunities for persons with disabilities or religious observances, including reasonable accommodation when needed. If you are hired by Accenture and require accommodation to perform the essential functions of your role, you will be asked to participate in our reasonable accommodation process. Accommodations made to facilitate the recruiting process are not a guarantee of future or continued accommodations once hired.
If you would like to be considered for employment opportunities with Accenture and have accommodation needs such as for a disability or religious observance, please call us toll free at 1 (877\) 889\-9009 or send us an email or speak with your recruiter.
Equal Employment Opportunity Statement
We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities.
For details, view a copy of the Accenture Equal Opportunity Statement
Accenture is an EEO and Affirmative Action Employer of Veterans/Individuals with Disabilities.
Accenture is committed to providing veteran employment opportunities to our service men and women.
Other Employment Statements
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Candidates who are currently employed by a client of Accenture or an affiliated Accenture business may not be eligible for consideration.
Job candidates will not be obligated to disclose sealed or expunged records of conviction or arrest as part of the hiring process. Further, at Accenture a criminal conviction history is not an absolute bar to employment.
The Company will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. Additionally, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the Company's legal duty to furnish information.
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Salary Context
This $87K-$266K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Accenture, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($176K) sits 19% below the category median. Disclosed range: $87K to $266K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Accenture AI Hiring
Accenture has 19 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span New York, NY, US, Columbus, OH, US, Morristown, NJ, US. Compensation range: $205K - $387K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
Frequently Asked Questions
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