Interested in this AI/ML Engineer role at Profit Recovery Partners LLC?
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About This Role
Wo*rk Location:*
At Profit Recovery Partners (PRP), we believe that collaboration and innovation thrive when we're together. This position is fully onsite at our Santa Ana office, allowing for real\-time teamwork, immediate problem\-solving, and a strong sense of community among our team members.
*Who You Are:*
As a Senior Database Developer – AI/ML Data Systems, you will be a senior member of our database development team, responsible for leading the design, development, optimization, and maintenance of complex database systems that power both traditional business applications and modern AI\-driven solutions. You will provide technical expertise and guidance to the team, drive innovation in AI\-enabled data engineering, and contribute to the overall data and AI strategy of the organization. In this role, you will collaborate with stakeholders, architects, data scientists, and engineering teams to deliver scalable, efficient, and secure database and AI data infrastructure. This position requires strong leadership skills, expert\-level knowledge of database management systems, hands\-on experience with AI/ML data technologies, and the ability to solve complex data challenges.
*What You Will Do:*
- Lead the design, development, and implementation of complex and highly scalable database systems, including data models, schemas, tables, views, and stored procedures, supporting both transactional workloads and AI/ML applications.
- Architect and implement data infrastructure for AI solutions, including vector databases, embedding stores, and semantic search capabilities (e.g., Azure AI Search, pgvector, Pinecone) to support retrieval\-augmented generation (RAG) and other LLM\-powered applications.
- Design and build data pipelines that prepare, transform, and serve high\-quality data for machine learning models, LLM fine\-tuning, and analytics, including feature engineering and feature store management.
- Integrate AI services and APIs (e.g., Azure OpenAI, Anthropic, OpenAI) into database\-driven applications and workflows, including intelligent document processing, natural language querying, and automated data classification.
- Leverage AI\-assisted development tools (e.g., GitHub Copilot, AI code assistants) to accelerate development, improve code quality, and champion responsible adoption of these tools across the team.
- Collaborate with cross\-functional teams, including software developers, data scientists, system administrators, and business analysts, to understand requirements, provide technical expertise, and deliver data and AI solutions that meet business objectives.
- Provide technical support and training to users and clients on the use of data systems, AI\-enabled tools, processes, and custom applications built.
- Conduct advanced performance tuning and optimization activities, including query optimization, indexing strategies (including vector indexing), and database configuration enhancements, to maximize system performance and efficiency. Develop and implement proactive monitoring solutions to detect and resolve performance issues, system bottlenecks, and database errors in a timely manner.
- Investigate and resolve complex database\-related issues, including data anomalies, performance bottlenecks, and system failures, using advanced troubleshooting techniques and tools.
- Contribute to data and AI architecture standards, guidelines, and governance frameworks, ensuring data consistency, integrity, interoperability, and responsible AI practices across the organization.
- Develop and implement robust security measures, access controls, and data encryption techniques to protect sensitive data including data used in AI systems and ensure compliance with industry regulations, data privacy requirements, and best practices.
- Provide technical leadership, mentorship, and guidance to junior members of the database development team, fostering a collaborative and knowledge\-sharing environment.
- Stay abreast of emerging database and AI technologies, trends, and industry best practices; evaluate their applicability to the organization's data landscape and propose innovative solutions.
- Develop and implement data quality assurance processes, data cleansing techniques, and data governance strategies to maintain high\-quality data for both operational systems and AI/ML training and inference.
- Design and implement robust data integration and ETL processes, leveraging advanced tools and technologies (e.g., Azure Data Factory, Informatica, Apache Kafka) to ensure seamless data flow and integration across multiple systems and sources.
- Provide expert\-level database administration, including capacity planning, backup and recovery strategies, disaster recovery planning, and high availability configurations.
- Perform other duties as assigned.
*What You Need:*
- 10\+ years of progressive experience in database development, administration, and optimization, with a focus on complex database solutions; recent hands\-on experience building or supporting AI/ML\-enabled data solutions strongly preferred.
- Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field preferred.
- Advanced certifications in database management systems or AI/data engineering (e.g., Microsoft Certified: Azure Data Engineer Associate, Azure AI Engineer Associate, Oracle Certified Master) preferred.
- Expert\-level proficiency in working with relational databases, preferably Microsoft SQL Server; demonstrated mastery of T\-SQL; and in\-depth knowledge of advanced SQL concepts, database performance tuning, and optimization strategies.
- Hands\-on experience with AI/ML data technologies, such as vector databases and embeddings (e.g., Azure AI Search, pgvector, Pinecone), retrieval\-augmented generation (RAG) architectures, and LLM APIs (e.g., Azure OpenAI, Anthropic, OpenAI).
- Proficiency in Python for data engineering and AI integration tasks, including working with data and ML libraries (e.g., pandas, LangChain or similar orchestration frameworks) alongside SQL\-based development.
- Extensive experience in designing and implementing complex data models, including conceptual, logical, and physical design phases, and a deep understanding of data architecture principles and best practices.
- Strong expertise in designing and implementing large\-scale data integration and ETL processes using advanced tools and frameworks (e.g., Azure Data Factory, Informatica, Apache Kafka).
- Familiarity with MLOps concepts and practices, including model data lifecycle management, monitoring data drift, and supporting model training and inference pipelines.
- Understanding of responsible AI principles, including data privacy, bias mitigation, and governance considerations for AI systems handling sensitive business data.
- Demonstrated leadership skills with the ability to lead and mentor a team of database developers, providing technical guidance, conducting code reviews, and fostering professional growth.
- Expert\-level proficiency in advanced database administration tasks, such as performance monitoring, capacity planning, backup and recovery strategies, and high availability configurations.
- Exceptional problem\-solving and analytical skills to identify and resolve complex database\-related issues and data challenges.
*Who We Are:*
Profit Recovery Partners is a management consulting firm specializing in enterprise cost optimization and spend reduction. We partner with FORTUNE 1000 corporations, leading law firms, private equity firms, and private enterprises across North America to design, implement, and sustain transformative cost\-reduction strategies. Leveraging deep category expertise, proprietary analytics, and supplier\-negotiation intelligence, PRP delivers measurable financial impact and operational efficiency. Our client engagements have generated more than $11 billion in verified savings, reinforcing PRP's reputation as a trusted advisor to executives seeking lasting performance improvement and sustainable cost reduction results.
Why Join Us:
At PRP, we offer more than just a job — we provide a dynamic, supportive environment where you can thrive personally and professionally. Here's what you can expect when you join our team:
- Comprehensive Benefits:
- Full medical, dental, and vision coverage
- Optional pet insurance
- Access to a gym membership discount (offered through our healthcare provider and available for select fitness centers)
- $200/month waived medical benefit for employees who opt out of our health plans
- Cell phone stipend for applicable roles
- Financial Wellness: 401(k) plan with company match
- Time Off to Recharge: Generous paid holidays, vacation, sick leave, bereavement, and jury duty leave
- Strong Foundation: Over 29 years of consistent growth and success
- Fun \& Connection: Annual summer retreat, holiday parties, happy hours, and themed celebrations throughout the year
- Career Growth: A collaborative, high\-performing team with frequent recognition and opportunities for internal promotion
- Our Culture: Fast\-paced, team\-driven, and fueled by a shared commitment to excellence
- Giving Back \& Getting Involved:
We're proud of our employee\-led committees that support causes and initiatives that matter to us all:
- *Corporate Social Responsibility*
- *Youth Rising Group*
- *Women's Empowerment Group*
- *Wellness Committee*
These groups offer meaningful opportunities to give back, connect with colleagues, and help shape a more inclusive and supportive workplace.
- Learn more at https://prpllc.com
The salary range for this position is: $118,640\.00\-$163,130\.00\. Actual compensation within the range will be dependent upon the individual's skills, experience, education, qualifications, and applicable employment laws.
Equal Opportunity Employer
Salary Context
This $118K-$163K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Profit Recovery Partners LLC, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($140K) sits 34% below the category median. Disclosed range: $118K to $163K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Profit Recovery Partners LLC AI Hiring
Profit Recovery Partners LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Ana, CA, US. Compensation range: $163K - $163K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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