Interested in this AI/ML Engineer role at NeuraFlash, Part of Accenture?
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About This Role
### Why NeuraFlash, Part of Accenture:
At NeuraFlash, Part of Accenture, we are redefining the future of business through the power of AI and groundbreaking technologies like Agentforce. As a trusted leader in AI, Amazon, and Salesforce innovation, we craft intelligent solutions—integrating Salesforce Einstein, Service Cloud Voice, Amazon Connect, Agentforce and more—to revolutionize workflows, elevate customer experiences, and deliver tangible results. From conversational AI to predictive analytics, we empower organizations to stay ahead in an ever\-evolving digital landscape with cutting\-edge, tailored strategies.
We are proud to be creating the future of generative AI and AI agents. Salesforce has launched Agentforce, and NeuraFlash, Part of Accenture, was selected as the only partner for the private beta prior to launch. Post\-launch, we’ve earned the distinction of being Salesforce’s \#1 partner for Agentforce, reinforcing our role as pioneers in this transformative space.
Be part of the NeuraFlash, Part of Accenture journey and help shape the next wave of AI\-powered transformation. Here, you’ll collaborate with trailblazing experts who are passionate about pushing boundaries and leveraging technologies like Agentforce to create impactful customer outcomes. Whether you're developing advanced AI\-powered bots, streamlining business operations, or building solutions using the latest generative AI technologies, your work will drive innovation at scale. If you’re ready to make your mark in the AI space, NeuraFlash, Part of Accenture is the place for you.
As a Technical Architect, Data \& AI focused on Salesforce Data 360 (formerly Data Cloud) and Agentforce, you will be responsible for owning the end\-to\-end technical solution that turns unified customer data into intelligent, AI\-powered experiences for our clients. You'll have the opportunity to make significant contributions to our success by working with the rest of our talented AI and Data team to delight customers.
This position is focused on architecting and executing technical solutions across the Data 360 and Agentforce platform—designing scalable data models, identity resolution strategies, real\-time activation patterns, and the agentic solutions built on top of them.
You'll have the opportunity to:
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- Lead and own the technical solution design across Data 360 and Agentforce—including data streams, DLOs, DMOs, identity resolution, calculated insights, segments, real\-time activation, and the agent topics, instructions, and actions that consume that data.
- Design scalable and secure data architectures on Salesforce Data 360—defining ingestion patterns, harmonization, identity resolution rules, and zero\-copy / data federation strategies.
- Deep dive into the limits and boundaries of Data 360 and Agentforce, finding novel solutions to drive business outcomes.
- Architect RAG and Generative AI solutions leveraging Unstructured DMOs, Retrievers, Search Indexes, and Document Processing, applying RAG best practices.
- Collaborate with customers, Project Managers, Solution Architects, Developers, and UX Designers to translate business needs into well\-architected solutions.
- Oversee and contribute to all stages of the project lifecycle—from initial design through development, testing, and final deployment.
- Define integration frameworks and reference architectures connecting Data 360, MuleSoft, enterprise data warehouses, and other systems using real\-time and batch patterns.
- Establish DevOps, source control, and CI/CD practices for data and AI solutions.
- Coach and mentor developers and junior architects, and follow and understand new Salesforce products and technical capabilities.
Required Qualifications
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- 6\+ years of experience in application, data, or software development and architecture
- 3\+ years of hands\-on experience with the Salesforce Platform in a development or architecture role
- Bachelor's or Master's degree in Computer Science, Data Engineering, or a related field
- Hands\-on experience architecting and implementing Salesforce Data 360 (Data Cloud)—data streams, DLOs, DMOs, identity resolution, calculated insights, and activation
- Proficiency in SQL and Apex, with exposure or working knowledge of modern coding languages (e.g., Python, JavaScript, Java)
- Experience designing data integration patterns across enterprise systems (MuleSoft, ETL/ELT tooling, data warehouses such as Snowflake, BigQuery, or Redshift)
- Understanding of data modeling, governance, security, and privacy best practices
- Experience with source control, branching/pull request strategies, and familiarity with CI/CD and DevOps principles
- Strong communication skills; the ability to convey complex technical details to all audiences
Preferred Skills
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- Hands\-on experience with Agentforce or other Generative AI / agentic platforms
- Experience building RAG solutions with Unstructured DMOs, Retrievers, and Search Indexes
- Salesforce certifications, preferably Data Cloud Consultant, Platform Developer II, Application Architect, or Agentforce Specialist
- Experience defining robust testing approaches for data and GenAI solutions
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 on 06/03/2026 and open for at least 3 days.
California Annual Salary Range
$94,400 \- $266,300 USD
Cleveland Annual Salary Range
$87,400 \- $213,000 USD
Colorado Annual Salary Range
$94,400 \- $230,000 USD
District of Columbia Annual Salary Range
$100,500 \- $245,000 USD
Illinois Annual Salary Range
$87,400 \- $230,000 USD
Maryland Annual Salary Range
$94,400 \- $230,000 USD
Massachusetts Annual Salary Range
$94,400 \- $245,000 USD
Minnesota Annual Salary Range
$94,400 \- $230,000 USD
New York / New Jersey Annual Salary Range
$87,400 \- $266,300 USD
Washington Annual Salary Range
$100,500 \- $245,000 USD
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.*
### What’s it like to be a part of NeuraFlash, Part of Accenture?
- Remote \& In\-Person: Whether you work out of our HQ in Massachusetts, one of our regional hubs, or you're one of over half of our NeuraFlash, Part of Accenture Family who work remotely, we’re focused on keeping everyone connected and unified as one team.
- Travel: Get ready to pack your bags and hit the road! For certain roles, travel is an exciting part of the job, with an anticipated travel commitment of up to 25%. So, if you have a passion for adventure and don't mind a little jet\-setting, this opportunity could be your ticket to exploring new places while making a positive impact on clients.
- Flexibility: Do you have to take the dog to the vet, pick up the kids from school, or the in\-laws from the airport? We know that a perfect 9\-5 isn’t possible. So you have to jump out to do any of those, no problem! We build a culture of trust and understanding. We value good work not the hours in which you get it done
- Collaboration: You have a voice here! If you work with a team of smart people like we do, it’s a no\-brainer to take suggestions and feedback on how to keep NeuraFlash, Part of Accenture thriving. Our executive team holds town halls \& company meetings where they address any suggestions or questions asked, no matter how big or small.
- Celebrate Often: We take our work seriously, but we don’t take ourselves too seriously. Whether it is an arm wrestling contest, costume party, or ugly holiday sweaters our teams love to have fun. And while we work hard, we don’t forget to slow down and celebrate the big things and the small things together.
Salary Context
This $87K-$245K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At NeuraFlash, Part of 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. This role's midpoint ($166K) sits 7% below the category median. Disclosed range: $87K to $245K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
NeuraFlash, Part of Accenture AI Hiring
NeuraFlash, Part of Accenture has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $245K - $245K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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