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About This Role
Description
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The Sr. Integration Architect, Agentforce Supply Chain is a hands\-on, domain expert responsible for architecting, designing, and governing the complex integration solutions that power major enterprise transformations within the Supply Chain and Manufacturing space.
This role focuses on integrating the Agentforce Agents to critical core systems (ERP, PLM, CRM) to ensure seamless data flow, process orchestration, and long\-term customer success. The Architect advises, designs, and performs architecture work, ensuring that all integration solutions adhere to industry best practices and shape the post\-implementation customer success landscape.
You will partner directly with clients, Delivery Managers, Solution Architects, and Account Teams to define the technical pathway for integrating the Salesforce ecosystem with the client's complex Backoffice infrastructure.
### Key Responsibilities
I. Integration Architecture \& Delivery Leadership
- Solution Design \& Best Practices: Advise, design, and manage enterprise transformation projects, architecting integration solutions in accordance with industry best practices and Salesforce Architecture methodologies.
- Hands\-on Architecture: Perform hands\-on architecture work, translating customer requirements into technical specifications, integration patterns, and secure system designs.
- System Proficiency: Maintain proficiency in core systems essential to the Supply Chain and Manufacturing domain (e.g., ERP, PLM, custom systems, CRM), understanding their data models, integration capabilities, and connectors.
- Mulesoft Proficiency: Leverage required Mulesoft experience to design and govern API\-led connectivity strategies, ensuring reusable assets and a secure integration layer.
- Technical Governance: Lead key discussions around change programs, providing governance and ensuring adherence to application lifecycle management (ALM) principles.
II. Agentic Enterprise \& Technical Innovation
- Agentic Integration: Design architectures that enable Agentic AI and Generative AI capabilities by ensuring secure, reliable data pipelines and API access between Agentforce and core enterprise systems.
- Data360 Integration: Utilize Salesforce Data360 for unifying, harmonizing, and activating customer data, preparing and enriching data for use in Generative AI and agent\-based applications.
- Orchestration: Apply advanced proficiency Agentforce and core systems for orchestrating complex business processes, especially those involving AI agents and automated workflows across multiple systems.
- Technical Consulting: Determine which Salesforce and third\-party technologies to leverage in customer\-driven architectures based upon product knowledge, industry experience, and standard integration frameworks.
### Required Skills and Qualifications
Technical Translation: Ability to understand business \& IT department requirements, translate those business needs into a compelling solution, and present recommendations to executive, technical, and business audiences.
- Enterprise Software: Deep knowledge of enterprise software applications and their delivery.
- Integration: Expertise in application lifecycle management, process orchestration, and enterprise integration principles and tools.
- Security: Strong grasp of network, application, and information security principles.
- Data \& Code Fundamentals:
+ Coding \& Scripting: Some coding experience and a willingness to work with code (Apex, Node, Go, R, Java or C\#, JavaScript, Python, Ruby, etc.).
+ Data \& Systems: Solid understanding of database and data management fundamentals, web and mobile application development, and system architecture.
Professional \& Strategic Competencies
- Problem Solving: Strategic problem solver, thought leadership, and comfort communicating with executive audiences.
- Customer Communication: Strong oral, written, presentation, and interpersonal communication and relationship skills.
- Continuous Learning: Lifelong learner, inquisitive, practical, and passionate about technology and sharing knowledge.
- Education: Bachelor’s or Master's degree in Computer Science, MIS, Software Engineering, Electrical Engineering, Data Science, Management Information Systems, other STEM degrees or equivalent work experience. Graduate study is a plus.
- Travel: Willing and able to travel domestically.
### Agentic AI \& Generative AI Platform Skills
The ideal candidate will have hands\-on experience with the following skills, which are critical for designing modern, AI\-powered solutions in this domain:
- Agentic AI: Direct experience with developing, deploying, and managing agentic AI frameworks. This includes proficiency with Agentforce and an understanding of how to build autonomous agents that can perform tasks, make decisions, and interact with various systems.
- Prompt Engineering \& Large Language Models (LLMs): Proven ability to design, refine, and optimize prompts to control the behavior of generative AI models.
- Generative AI Architecture: Experience in architecting and integrating generative AI solutions into enterprise systems (API gateways, model management platforms, data pipelines).
### Preferred Qualifications
- 8\-10 years of experience in IT architecture, solution engineering, or a related field, with 5\+ years dedicated to integration architecture in a customer\-facing or internal delivery role.
- Mulesoft Certification (Integration Architecture Designer is a significant plus).
- Experience in roles that required working hands\-on to build, implement, administrate, or maintain enterprise software, specifically in the Supply Chain or Manufacturing domain.
- Understanding of risk mitigation, compliance, and security in the context of enterprise software.
- Knowledge of the Salesforce platform, including relevant Salesforce Certifications and/or Trailhead Superbadges is a plus.
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Informatica, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Informatica AI Hiring
Informatica has 39 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Architect, AI Software Engineer. Positions span IN, US, CA, US, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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