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About This Role
Technology Architecture Manager \| Senior Level \| Full time
Job No. R00340177 \| Multiple Locations
We Are:
Accenture’s Salesforce practice, and we live to see what this platform can do. Since we were one of the first on the scene, we’re the largest global Salesforce practice, not to mention one of the best. In fact, we are the industry\-leader for building Salesforce solutions, and we are Master Certified in all 12 industries and clouds. We use tech like Lightning, Einstein, and Bolt to build stronger, better relationships with customers. And we are always learning. We’re building the most resourceful team on the planet, and helping our people with new skills, training, and support to get over 4,200 certifications including Certified Technical Architect (CTA). Visit us here to find out more about Accenture's Salesforce practice.
Who You Are
You are a Data Cloud and AI Tech Lead within Accenture’s Life Sciences Salesforce Business Group, operating at the layer between architecture and execution. You translate what senior architects design into what delivery teams build — owning solution design documents, leading technical workstreams, and making sure the architecture holds through the full program lifecycle.
You are fluent in Salesforce Data Cloud, Agentforce, and Life Sciences CRM platforms. You thrive in complexity: multi\-workstream programs, distributed teams, and client environments where the data is messy, the timelines are tight, and the architecture decisions matter. You bring clarity to ambiguous technical problems and lead junior team members without waiting to be asked.
The Work
- Solution design ownership — lead end\-to\-end solution design across assigned workstreams, including data model, integration patterns, security model, and platform configuration; keep SDDs, architecture decision records, and data flow diagrams current throughout delivery
- Agentforce \& AI delivery — own technical design and build for Agentforce use cases — patient services agents, HCP engagement agents, Voice AI integrations — across active Life Sciences programs
- Data Cloud workstreams — lead Data Cloud implementation tracks including data ingestion, identity resolution, unified profile design, segmentation, and activation; identify and resolve data quality and governance issues
- Integration delivery — design and implement integrations between Data Cloud, CRM, external data sources, and Agentforce capabilities; make architecture tradeoffs across middleware options (MuleSoft, REST/SOAP, Salesforce Connect)
- Bridge function — serve as the connective layer between functional requirements and technical delivery; align architects, developers, and client stakeholders on design decisions and scope boundaries
- Technical leadership — lead design sessions, code reviews, and deployment governance; manage solution risks, design gaps, and cross\-workstream dependencies; escalate proactively
- Team development — guide and mentor L8 architects and analysts; set technical standards across the team and build capability in Data Cloud and AI delivery practices
- Pre\-sale contribution — support estimation, scoping, and staffing conversations during pursuit phases; contribute to architecture sections of RFP responses and orals
- Practice contribution — build accelerators, methodology assets, and LS\-specific AI use case patterns that scale across the practice
What You Need:
- Minimum of 6 years of Salesforce delivery experience, with meaningful time in both functional and technical roles on full\-lifecycle implementations
- Minimum of 2 years experience as a Solution Architect or senior technical lead on at least two end\-to\-end Salesforce programs
- Minimum of 2 years working knowledge of data architecture, identity resolution, segmentation, and real\-time activation patterns
- Minimum of 1 year hands\-on experience designing and implementing Agentforce use cases, including AI\-powered workflow automation
- Integration experience — practical experience designing integrations across CRM, data platforms, and external systems
- Minimum of 2 years experience in client\-facing consulting environments leading technical workstreams in Agile or hybrid delivery frameworks
- Bachelor’s degree in Computer Science, Engineering, or equivalent (minimum 12 years) work experience. If Associate’s Degree, must have minimum 6 years work experience
Bonus Points:
- Experience with Salesforce Life Sciences Cloud, Health Cloud, or equivalent pharma/biotech CRM; understanding of commercial and patient\-facing use cases
- Experience designing or delivering Agentforce Voice, contact center AI, or conversational agent solutions in Life Sciences
- Familiarity with Veeva Vault CRM, Veeva CRM, or the Veeva\-to\-Salesforce migration landscape and integration patterns
- Agentforce Specialist, Data Cloud Consultant, Sales/Service Cloud Consultant, Health Cloud Accreditation, or Certified Application Architect
- Experience managing technical delivery across onshore/offshore distributed teams
- Prior experience contributing to RFP responses or solution design in a pursuit context
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 08/15/2026\.
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
New York City, NY
Albany, NY
Arlington, VA
Atlanta, GA
Austin, TX
Beaverton, OR
Bentonville, AR
Boston, MA
Carmel, IN
Charlotte, NC
Chicago, IL
Cincinnati, OH
Cleveland, OH
Columbus, OH
Culver City, CA
Denver, CO
Des Moines, IA
Detroit, MI
Hartford, CT
Houston, TX
Irving, TX
Kirkland, WA
Miami, FL
Milwaukee, WI
Minneapolis, MN
Morristown, NJ
Mountain View, CA
Nashville, TN
Oklahoma City, OK
Overland Park, KS
Philadelphia, PA
Pittsburgh, PA
Raleigh, NC
Redmond, WA
Sacramento, CA
San Diego, CA
San Francisco, CA
Scottsdale, AZ
Seattle, WA
St. Louis, MO
St. Petersburg, FL
Walnut Creek, CA
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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 Logic, Inc., 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. Mid-level AI roles across all categories have a median of $200,000. 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.
Logic, Inc. AI Hiring
Logic, Inc. has 17 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, AI Architect. Positions span Seattle, WA, US, New York, NY, US, Columbus, OH, US. Compensation range: $205K - $387K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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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