Why AI engineers Talent Is So Hard in 2025 — and How to Hire AI Talent Successfully

It has never been more competitive to hire AI developers. Companies across nearly every industry need AI professionals right now. Every business organization wants to build its own generative AI features. Every startup needs machine learning engineers. Every company needs MLOps talent and expertise. The numbers paint a bleak picture: for every qualified AI candidate, there are 15-20 open AI positions for hire. Universities produce about 40,000 AI graduates worldwide each year, while the AI market requires over 500,000 new AI positions per year.

When you are hiring AI talent, you are positioning yourself against some colossal players. Companies like Google, Meta, Amazon, and Microsoft are offering compensation packages of over $500,000 for a senior machine learning engineer. Startups have a tough time getting remotely close to those numbers.

Remote work changed everything. A company in Mumbai is now in competition for the same AI developer as a San Francisco firm. The influx of global hiring obviously created opportunities for potential companies, but it also created higher levels of competition.

The acquisition of AI talent has to contend with something else: a moving target. The skills required to compete today may not be the skills required in six months. Experts people thought they could count on for traditional ML in the past must now know about generative AI. Experts in natural language processing (NLP) must be willing to embrace large language models. Data scientists want to develop MLOps capabilities.

Additionally, the new areas of specialization in this industry are developing at all times:

• Experts in prompt engineering
• Specialists in fine-tuning large language models (LLMs)
• Architects in agentic AI
• Engineers in AI safety
• Developers in multimodal AI

The salary expectations have also risen to an unprecedented level. Entry-level professionals in the AI space are expecting a base salary of between $120,000-$150,000. Mid-level machine learning engineers are expecting between $180,000-$250,000. Senior AI talent is asking for between $300,000-$600,000 in total compensation.

The need for equity is at an all-time high, too. AI professionals are looking for tangible product ownership in the work they produce, as well as realistic research budgets, conferences, and publication opportunities available to them.

Adoption of GenAI and Deep Learning by Industry: Out of nowhere, every company suddenly requires some sort of AI capability. Healthcare companies are building diagnostic systems, financial firms are building fraud detection models, retailers are building recommendation engines, and manufacturing firms are using predictive maintenance.

Generative AI changed everything. The success of ChatGPT led to the mainstream adoption of AI. Now, every CEO is seeking their own company voice and AI product. Such a universal need has placed unprecedented strain on the recruitment of AI talent.

AI experts mainly gather in specific localities. Most AI experts are concentrated in Silicon Valley, New York, London, Toronto, and Bangalore. Most communities have a challenge hiring AI engineers locally.

Most AI experts emerge from leading educational institutions. Stanford, MIT, Carnegie Mellon, Berkeley, and a small handful of global institutions produce disproportionately larger shares of AI talent. The concentration of AI talent creates a hyper-competitive environment for vendors seeking AI talent.

Machine learning engineers create systems that can apply AI in production. They develop the model architecture. They optimize the training pipeline. They can ensure large-scale, reliable model performance.

When bringing in AI talent with ML experience, look for:

• Experience with deep learning frameworks (PyTorch, TensorFlow).
• Experience optimizing models and/or compression.
• Experience with distributed training.
• Knowledge of model evaluation metrics.
• Production experience with AI.

Data engineering is the foundation. Data engineering can create a robust solution, but without good pipelines, AI projects can fail. Data scientists link a business problem with a potential technical solution.

Important capabilities include:

• Develop scalable ETL pipelines.
• Feature engineering and/or feature selection.
• Monitoring data quality.
• Quantitative analysis expertise umbrella.
• Understanding organizational metrics.

Generative AI produced entirely new roles. LLM specialists will fine-tune foundation models for specific use cases. Prompt engineers will optimize the interaction with those models.

Key skills:

• Fine-tuning large language models
• Retrieval-augmented generation (RAG) systems
• Prompt optimization and testing
• Token efficiency and cost-benefit modelling
• Natural language processing fundamentals

MLOps is the combination of development and operations. These professionals can ensure AI systems run reliably in production. They keep an eye on model performance. They manage retraining, or re-instruction, cycles.

Essential skills:

Container orchestration (K8s, Docker) Versioning models and tracking experiments Automated retraining pipelines Monitoring performance and alerting Cloud platforms (AWS, GCP, Azure)

Ethical AI practices are more relevant than ever. Companies may need professionals who will verify that AI systems are fair, transparent, and in compliance.

Key Responsibilities:

• Detection and mitigation of bias
• Implementation of model explanation
• Compliance (GDPR, AI Act)
• Assessment of vulnerabilities through penetration testing
• Development of an ethical AI framework

AI professionals want to work on projects that are relevant to a problem. No overlap with the phrase "AI for AI's sake." They want projects where AI actually drives value.

The talent asks:

• Will the AI initiative help real users?
• Does the company have quality training data?
• Are we building products, or research prototypes?
• What is the path from a model to a production system?

The very best AI developers want to have the freedom of their creative process. They want to explore alternative solutions beyond, and autonomous freedom to determine technology and methodologies.

Winning cultures provide:

• Exploration of undefined or unknown solutions
• Supporting professional conferences
• Time for their research and learning
• Encouragement to participate in open-source
• Opportunities to connect with other disciplines

Compensation is always important. However, sourcing AI talent requires more than a competitive salary. Equity still matters. Learning budgets still matter, and publication grants matter.

Attractively competitive packages would include:

• A fair market-based salary
• Equity grants that are meaningful
• Annual learning stipend ($5k-$10k)
• Attendance at conferences
• Annual time to write and publish
• Funding to publish

Generic job descriptions do not cut it. Successful companies that seek to hire AI talent do so with specificity. Which technologies are you looking for? Be explicit about your exact technical needs! What is the particular business problem you aim to solve? What is your AI system's architecture?

Job Description Composite:

• Specific AI/ML technologies needed
• Descriptions of actual projects (as opposed to buzzwords)
• Infrastructure and tech stack
• Team structure/collaboration
• Specific metrics of impact

Resumes and interviews deceive. Skills assessments tell the truth about the individual’s actual capabilities. When you hire an AI developer, evaluate and ascertain their performance capabilities!

Assessment Detail:

• Take-home challenge: building an ML model (4-6 hours)
• Code review and optimization task
• Designing a system for AI applications
• Evaluating and debugging a Model
• Solve a real production situation discussion

Lengthy hiring timelines lose candidates. Top AI talent has multiple offers. It’s about speed. Reduce your time to hire from 45 days to 21 days.

Interview Process Streamlining:

• Initial screening call (30 mins)
• Technical assessment (async)
• Technical deep-dive interview (90 mins)
• Team fit and project discussion (60 mins)
• Decision on offer to hire (within 48 hours)

AI professionals want to invest in organizations when they see vision. Demonstrate to them that you have a strategy for AI. Show them the level of technical sophistication. Show them you can tackle AI challenges.

What to Show:

• Roadmap for AI products (6-18 months)
• Current AI infrastructure and plans
• Research partnerships or initiatives
• Examples of past successful AI projects

Contract and fractional AI talent fills immediate gaps. This model provides you with flexibility. You will have access to expertise without long-term commitment.

Good Situations to Use On-Demand AI Developers (Ghosts):

• "Proof-of-concept" projects.
• Specific technical challenges.
• Seasonal capacity needs.
• Requires specialized skills: typical projects can be 6-12 weeks.

Some AI hiring firms have a network of complete AI teams. AI teams include data engineers, ML engineers, and MLOPs professionals, working together. This model allows you to fine-tune project specifications.

AI Teams-as-a-Service Advantages (Ghosts):

• You can deploy teams quickly.
• All members have experience with one another.
• Workflows are planned (think sprints).
• Risk is less when more teams know the company and you, the customer.
• Appreciation of gradual capacity expansion.

Universities are a source of cutting-edge research on AI. This modality is based on partnering with out-of-state/university/college research labs. You can be a sponsor for PhD students and develop internship pipelines.

Partnership Models (Ghosts):

• Sponsored research projects.
• Create adjunct positions at your company for the PhD students.
• Create internship programs that will blossom into hiring.
• Guest lectures.
• Building publication opportunities jointly.

A lot of your engineers can learn AI. If you believe you have a core that has strength, you invest in a series of learning programs. Software engineers can become machine learning engineers. Data analysts can become data scientists.

Up Skills Framework (Ghosts):

• Structured learning programs that focus on 3-6 months.
• Altering education with real projects in place.
• Mentors or experienced AI professionals.
• Support you with certifications.
• Gradual responsibility.

"AI Engineer" reads as vague. Be precise. Define the role. State the technology needed.

Poor Example: "Looking for an AI engineer to help build ML models"

Better Example: "Senior ML Engineer to build production recommender systems with Pytorch, deployed on AWS with real-time inference"

Hiring AI is expensive. Face that fact. When you look to save, you will fail. Plan accordingly.

Six interview rounds will lose candidates. Respect their time. Make an assertive hiring decision.

AI talent wants to work on modern tech, not legacy technology. Legacy and outdated tech are a massive deterrent to talent. Invest in modern infrastructure before hiring AI talent.

AI systems reflect their creators' biases. Diverse teams build fairer systems. Homogeneous teams create problematic AI.

Research shows diverse AI teams:

• Identify bias issues 40% faster
• Create more robust model architectures
• Consider broader use cases
• Deliver products serving wider audiences

Diversity Hiring Strategies:

• Partner with diverse coding bootcamps
• Remove degree requirements where possible
• Blind resume screening
• Structured interview questions
• Diverse interview panels

Roles in AI in the future will likely focus on human-AI collaboration. AI professionals will likely work on systems that enhance human skills. Pure automation may become less popular.

AI is changing every month. To hire AI talent that remains engaged, it will be critical to support a learning environment. Show support to those learning to update their skills, making sure they have time to update their skills before launching into a new project.

Essential Learning Support:

• Dedicated learning time (4-6 hours weekly)
• Online course subscriptions
• Conference attendance (2-3 annually)
• Internal knowledge sharing sessions
• Experimental project allocation