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About This Role
Description
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Overview of the Role
We are looking for a technical Product Leader to guide the future of "Customer Zero" for Salesforce’s internal implementation of Agentforce for Sellers. In this role, you will own the end\-to\-end product lifecycle for one of our critical Agent pillars, transforming how our sellers engage with customers and close deals.
As part of the Global Business Growth \& Automation (GBGA) team, we are at the forefront of innovation within Salesforce. We partner with our internal product, business technology, sales, and revenue teams to deliver cutting\-edge tools and platforms for Salesforce employees. We are an agent\-first organization, using our own technology to augment our workforce with autonomous agents. We work deeply within the Salesforce platform to ensure that we are Customer Zero (we use our own products), making the most of the Salesforce platform by rapidly launching new features to our employee base as they are released.
The Product Innovation \& Architecture team is dedicated to driving product innovation \& optimizing Salesforce solutions to deliver an unparalleled Lead\-to\-Cash (L2C) experience. We embody the principles of Customer Zero with a persona\-centric, agent\-first approach to scale solutions.
Role Specialization
- Sales Agent – Account Intel, Research, \& Strategy Agents
+ Focus: building agents that act as a super\-analyst for every seller.
Responsibilities
1\. Product Strategy \& Governance (The "What" \& "Why")
- Own the Agent Charter: Define the "Soul" of the agent. Responsible for defining Agent's persona, authority levels (Read vs. Write vs. Nudge), and routing logic.
- Manage the Council and Strategy: Represent your Agent’s scope and roadmap to the Sales Agent Council, ensuring alignment with the broader ecosystem and preventing collision with other agents.
- Cross\-Agent Orchestration: Partner with other Agent Owners to ensure seamless "handshakes" between the all Sales\-facing agents so the customer experience is unbroken.
- AI Evangelism: Educate broader internal teammates on the "realm of possibility" for Agentforce, driving awareness and adoption across the broader organization.
2\. Technical Co\-Development (The "How")
- Platform Architecture: Leverage deep knowledge of Salesforce Core \& Data Cloud to fuel the agent, determining *where* the data lives and *how* the agent accesses it.
- Prompt Engineering \& Tuning: Sit side\-by\-side with DET Engineers to write system prompts, test "Golden Paths," and tune agent responses in real\-time.
- Data Integrity: Partner with the Content and Data management teams to ensure your agent is ingesting "human\-ready" content and treating it as "machine\-ready" truth.
3\. Execution \& Adoption
- Lifecycle Ownership: Drive the full Agent Development Lifecycle: JTBD \> Charter \>Build \> Test \> Monitor \> Tune.
- Analyst Guidance and Backlog: Guide the work of Agent Business Analysts to produce detailed test scripts, conversation logs, and edge\-case mappings, while maintaining a healthy, prioritized project backlog that aligns with the L2C roadmap.
- Feedback Loop: Monitor feedback channels and telemetry. Go beyond simple bug tracking by constantly analyzing agent responses for drift and performance degradation. Interpret these patterns to distinguish between minor tweaks and critical behavioral gaps that drive roadmap pivots.
Required Skills/Experience
Technical Expertise and AI Fluency
- Salesforce Platform Mastery: 6\+ years of experience experience in Salesforce Platform architecture, including governance, and globally scaled solutions. You must be fluent in Flow, Object Modeling, Permissions, and Data Cloud.
- AI/LLM Fluency: Experience with Generative AI, Prompt Engineering, and Conversational Design.
- Learning Obsessed: Committed to continuous learning to ensure the team remains at the forefront of AI technology within the L2C employee experience.
- Design Thinking: Expert level problem solving skills with a competency in design thinking and creative solutioning.
Consulting and Strategic Mindset
- Ambiguity Navigator: Ability to work with ambiguity and deliver results in "grey areas," pivoting quickly as AI technology evolves.
- Strategic alignment: Ability to define practical solutions that align with corporate strategy.
- Consultative Approach: Ability to translate customer needs into user stories and prototypes with high attention to detail, organization and process.
- Sales Empathy: A keen eye for user experience, mixed with an understanding and empathy for the sales process.
Communication and Leadership
- Executive and Technical Communication: Strong ability to draft and deliver vision to all levels of executive leadership, balanced with detailed technical articulation for development teams.
- Cross\-Functional Leadership: Ability to facilitate leadership discussions, drive consensus on prioritization, and communicate program changes effectively.
- Influence \& Alignment: A collaborative leader capable of gathering diverse teams, aligning them on common goals, and driving action across peer groups without direct authority.
- Storytelling: Ability to use storytelling to explain complex concepts in language that connects with stakeholders.
Collaboration and Soft Skills
- Works well with others: This is a leadership role that requires the ability to gain trust and alignment of a variety of teams across the business. Must be able to gather people together and align them on a common goal, then influence and drive action in peer teams.
- Active Listening: Attentive to others’ points of view; demonstrates strong listening and questioning techniques to deeply understand underlying issues.
- Team Engagement: An inquisitive, positive, "roll\-up\-your\-sleeves" attitude with a focus on building trust and positive personal branding within the team.
Desired Skills/Experience
- Product Leadership: Proven experience (Manager\+) owning a roadmap, prioritizing backlogs, and managing senior stakeholders (Directors/VPs).
- Sales Domain Experience: Deep understanding of the L2Clifecycle and empathy for the day\-to\-day reality of a Seller.
- Data Compliance Policy Experience: Experience with Policy adherence, this role is accountable to vetting solutions within our Legal, Security \& Ethics policies.
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. Mid-level AI roles across all categories have a median of $131,300.
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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