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About This Role
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Precisely is the leader in data integrity. We empower businesses to make more confident decisions based on trusted data through a unique combination of software, data enrichment products and strategic services. What does this mean to you? For starters, it means joining a company focused on delivering outstanding innovation and support that helps customers increase revenue, lower costs and reduce risk. In fact, Precisely powers better decisions for more than 12,000 global organizations, including 95 of the Fortune 100\. Precisely's 2500 employees are unified by four company core values that are central to who we are and how we operate: Openness, Determination, Individuality, and Collaboration. We are committed to career development for our employees and offer opportunities for growth, learning and building community. With a "work from anywhere" culture, we celebrate diversity in a distributed environment with a presence in 30 countries as well as 20 offices in over 5 continents. Learn more about why it's an exciting time to join Precisely!
Precisely is an AI\-first organization. All employees are expected to demonstrate proficiency in applying AI tools to accelerate their work, improve output quality, and eliminate low\-value tasks. Candidates should be comfortable using generative AI tools (e.g., Microsoft Copilot, ChatGPT) in their day\-to\-day workflows, able to evaluate AI\-generated outputs critically, and open to continuously adopting new AI capabilities as they emerge.
This position is 100% remote anywhere in the US.
Overview:
The Professional Services AI Enablement Analyst plays a critical role in accelerating the effective adoption of AI tools and capabilities across Precisely’ s Professional Services team and is designed for bridging the gap between cutting\-edge AI and billable service delivery. The role will collaborate with technical, business and operational teams to identify AI use cases, support implementation, and measure impact for Professional Services, ensuring AI solutions are practical, scalable, and aligned with business objectives. The Analyst is comfortable translating complex AI concepts into actionable insights and supporting stakeholders as AI becomes part of everyday workflows.
What you will do:
- Building AI Deployment Accelerators:
- + Independently build AI agents to automate complex data mapping, schema conversion, and code migration tasks.
+ Design "Configuration Agents" that auto\-generate documentation and environment setups for product deployments.
- Use Case Identification \& ROI Strategy:
- + Audit Professional Services workflows to identify high\-impact AI opportunities.
+ Perform cost\-benefit analyses, translating technical metrics into "hours saved" and "margin improvement" for leadership.
- Hands\-on Agentic Engineering:
- + Design and code autonomous AI Agents using frameworks like Lang Graph, Crew AI, or Semantic Kernel.
+ Build systems capable of multi\-step reasoning, self\-correction, and tool\-calling.
- Team Enablement \& Training:
- + Collaborate with Central AI Team's AI Enablement Lead on PS\-specific training content
- Self\-Driven Governance \& Scaling:
- + Implement LLM Ops for PS\-specific agentic solutions, operating within Precisely's AI Architecture Standards and in coordination with the Central AI Team's AI Platform Engineer for tooling, evaluation standards, and model governance
- Agentic Solutions:
- + Leads the strategy, design, and delivery of agentic service offerings in Professional Services by leveraging MCP servers to connect AI agents with enterprise data and software solutions.
+ Owns customer adoption of MCP based solutions, driving scalable, governed, and LLM agnostic agent implementations that maximize existing automation investments.
What we are looking for:
- Bachelor’s degree in computer science, Information Systems, Data Analytics, Business, or a related field
- 3–5 years of experience supporting Professional Services delivery, consulting initiatives, or technology\-enabled programs in a software or SaaS environment
- Experience supporting technology adoption, change management, or enablement across cross\-functional teams
- Strong analytical and problem\-solving skills with the ability to interpret data and derive actionable insights
- Excellent written and verbal communication skills, with the ability to translate complex concepts for diverse audiences and non\-technical stakeholders. Ability to simplify complex LLM concepts into actionable training for consultants, analysts, and partners.
- Experience working with stakeholders at multiple levels, including technical and non\-technical partners
- AI Enablement \& Training background and excellent presentation skills a must
Required Skills:
- Strong proficiency in Python, with hands\-on experience building or enhancing AI\-enabled workflows or internal accelerators
- Practical experience designing and implementing generative AI solutions, including Retrieval\-Augmented Generation (RAG), vector databases, and API\-based integrations
- Experience working with AI agent frameworks and orchestration tools such as LangGraph, CrewAI, Semantic Kernel, or LlamaIndex
- Experience using enterprise LLM platforms (e.g., Anthropic Claude, OpenAI models), with sound judgment in evaluating outputs, limitations, and risk
- Solid working knowledge of AI/ML concepts and automation platforms sufficient to support enablement, training, and real\-world solution delivery
AI Skills/Knowledge:
- Ability to identify and improve manual or repetitive Professional Services processes using AI or automation
- Clear analytical and communication skills, with the ability to explain AI\-driven outcomes to consultants and delivery stakeholders
- Comfort using generative AI tools (e.g., Microsoft Copilot, ChatGPT, Claude) and a growth mindset aligned to an AI\-first organization
\#LI\-REMOTE \#LI\-CA1
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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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Precisely, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Precisely AI Hiring
Precisely has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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