Interested in this AI/ML Engineer role at IFS?
Apply Now →Skills & Technologies
About This Role
Company Description
IFS is a billion\-dollar revenue company with 7000\+ employees on all continents. Our leading AI technology is the backbone of our award\-winning enterprise software solutions, enabling our customers to be their best when it really matters–at the Moment of Service™. Our commitment to internal AI adoption has allowed us to stay at the forefront of technological advancements, ensuring our colleagues can unlock their creativity and productivity, and our solutions are always cutting\-edge.
About TheLoops
TheLoops, an IFS company, is the first enterprise\-grade AI Agent platform built for mission\-critical applications and industries. Our AI Agents act as digital coworkers that are governed, secure, always learning, and working 24/7 to drive measurable business outcomes.
As the company grows, it is looking for mission\-driven individuals to help deliver the future of AI\-powered operations.
Job Description
As a Software Engineer, AI/ML, you will design, build, and optimize the backend systems that power intelligent agent workflows. You will work across data pipelines, APIs, and AI/ML frameworks to create reliable, scalable, and production\-ready solutions. This role emphasizes strong software engineering fundamentals, with the added dimension of applying AI/ML concepts to real\-world enterprise applications. What You'll Do:
- Backend \& Systems Engineering
- Build and maintain Python\-based services, integrations, and data pipelines that support AI agent functionality.
- Develop reusable libraries, APIs, and frameworks to accelerate AI\-driven product capabilities.
- Ensure code quality, maintainability, and scalability through testing, CI/CD, and performance monitoring.
- Applied AI/ML Engineering
- Implement and optimize workflows leveraging LLMs, embeddings, RAG systems, and vector databases.
- Integrate AI/ML libraries and external APIs (e.g., OpenAI, Hugging Face, LangChain, Pinecone, Weaviate).
- Experiment with prompt engineering and fine\-tuning to improve reliability and performance of deployed agents.
- Collaboration \& Delivery
- Partner with product and core engineering teams to translate requirements into technical solutions.
- Contribute to architecture decisions and internal technical documentation.
- Support the deployment of agents into enterprise environments with a focus on stability, accuracy, and scale.
Qualifications
- 2–5 years of professional experience as a Python Engineer / Backend Engineer (experience with AI/ML is a strong plus).
- Strong proficiency in Python and familiarity with JavaScript/TypeScript for integrations.
- Hands\-on knowledge of AI/ML frameworks and tools (OpenAI, Hugging Face, LangChain, vector DBs, RAG).
- Understanding of system integration patterns and comfort working with RESTful APIs, JSON, and data pipelines
- Experience with enterprise systems (CRM, ERP, Helpdesk, Developer platforms, HR/Finance systems) is a plus.
- Strong debugging, testing, and optimization skills.
- Ability to write clean, maintainable, and well\-documented code.
What Success Looks Like in 3–6 Months
- Technical Depth: You’ll be proficient in the internal platform, building integrations and Python services with confidence.
- AI/ML Impact: You’ll have delivered backend components that integrate AI/ML capabilities into enterprise workflows.
- Engineering Excellence: You’ll contribute to reusable libraries, deployment pipelines, and system reliability practices.
- Team Contribution: You’ll collaborate effectively with other engineers and product teams, influencing technical direction.
Additional Information
At IFS you will work in a growing, global enterprise software company where informal hierarchies promote simplicity and trust with responsibility. We strive to deepen our expertise by taking on challenging and varying work assignments in a collaborative environment together with committed colleagues.
With professionalism and a strong customer focus in everything we do, from R\&D to Sales and Consultancy, we are proud to serve world\-class customers all over the globe with our leading products.
If you want to change the status quo, we'll help you make your moment. Join Team Purple. Join IFS.
What We’re Offering
- Salary Range: $110,000 to $120,000 plus bonus potential
- Flexible paid time off, including sick and holiday
- Medical, dental, \& vision insurance
- 401K with Company contribution
- Flexible spending accounts
- Life insurance and disability benefits
- Tuition assistance
- Community involvement and volunteering events
M/F/Disabled/Vet VEVRAA Federal Contractor. We are a Drug\-Free Workplace. Interested candidates should apply at: www.ifs.com/about/careers\-at\-ifs
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or status as a protected veteran. VEVRAA Federal Contractor, Equal Opportunity Employer
Salary Context
This $110K-$120K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At IFS, 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. This role's midpoint ($115K) sits 31% below the category median. Disclosed range: $110K to $120K.
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.
IFS AI Hiring
IFS has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Palo Alto, CA, US, Itasca, IL, US. Compensation range: $120K - $120K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.