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About This Role
### Responsibilities
- Define end\-to\-end architecture for AI/ML and Gen AI, Agentic AI, MCP systems including data pipelines, model training/inference, and MLOps.
- Serve as a strategic advisor / consultant to clients, leading solution design discussions, presenting AI/ML architectures, and representing 3Pillar in client\-facing interactions to drive innovation and business value.
- Architect scalable solutions using cloud\-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI).
- Lead the integration of Generative AI into components / features leveraging LLMs into enterprise applications using APIs (OpenAI, Gemini and others) or open source models like LLama.
- Design retrieval\-augmented generation (RAG) systems with vector databases (Pinecone, Weaviate, FAISS and similar).
- Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining.
- Evaluate build\-vs\-buy decisions and benchmark AI tools/platforms.
- Evaluate emerging technologies and trends in AI, ML, Gen AI, Agentic AI space and recommend adoption strategies.
- Mentor technical teams and guide solution architects, AI/ML engineers.
- Ensure ethical and responsible AI practices including bias detection, interpretability, and governance.
- Act as a subject matter expert, providing guidance and mentorship to junior colleagues and strengthen the expertise in the organization.
- Collaborate with leadership to align machine learning strategies with overall business objectives.
- Represent the organization at industry conferences, workshops, and events.
- Drive cross\-functional collaboration to identify opportunities for applying machine learning to business challenges.
### Minimum Qualifications
- Master’s degree in Computer Science, Engineering, Mathematics, or a related field with 15\+ years of industry experience with strong IT services background in machine learning, data science, AI, with a track record of delivering impactful projects; a Ph.D. is highly desirable.
- Strong grasp of AI architecture patterns (RAG, Agent AI, MCP based systems, prompt orchestration).
- Deep experience with Python, ML libraries (scikit\-learn, XGBoost, PyTorch, TensorFlow).
- Hands\-on with Gen AI APIs (OpenAI, Claude, Gemini), prompt engineering, embeddings, and fine\-tuning.
- Experience designing enterprise AI systems with MLOps (MLflow, Kubeflow, SageMaker Pipelines).
- Familiarity with APIs, microservices, and containerization (Docker, Kubernetes).
- Experience in Data Governance, Model Risk Management, and compliance.
- Extensive knowledge of machine learning theory, algorithms, and methodologies.
- Strong leadership and communication skills, with the ability to influence stakeholders at all levels of the organization.
- Demonstrated ability to think strategically and drive innovation.
### Additional Experience Desired
- Good undertaking of Software Product Development and how to integrate AI ML components into a software product.
- Experience in working with huge datasets in a production environment.
- Published research in top\-tier conferences or journals is a plus.
### What is it like working for 3Pillar Global?
At 3Pillar, we offer a world of opportunity:
- Imagine a *flexible work environment* – whether it's the office, your home, or a blend of both. From interviews to onboarding, we embody a remote\-first approach.
- You will be part of a *global team*, learning from top talent around the world and across cultures, speaking English everyday. Our global workforce enables our team to leverage global resources to accomplish our work in efficient and effective teams.
- We’re *big on your well\-being* – as a company, we spend a whole trimester in our annual cycle focused on wellbeing. Whether it is taking advantage of fitness offerings, mental health plans (country\-dependent), or simply leveraging generous time off, we want all of our team members operating at their best.
- Our professional services model enables us to *accelerate career growth* and development opportunities \- across projects, offerings, and industries.
- *We are an equal opportunity employer.* It goes without saying that we live by values like Intrinsic Dignity and Open Collaboration to create cutting\-edge technology AND reinforce our commitment to diversity \- globally and locally.
Join us and be a part of a global tech community! \\uD83C\\uDF0D\\uD83D\\uDCBC Check out our Linkedin site and Careers page to learn more about what it’s like to be part of our \#oneteam!
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At 3Pillar Global, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 11% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
3Pillar Global AI Hiring
3Pillar Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $180K - $180K.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% of all AI roles offer remote work.
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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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