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About This Role
We are seeking a motivated Junior AI Engineer to join our growing AI \& Data Team. This role offers an opportunity to work alongside experienced consultants, architects, data scientists, and AI engineers to design, develop, and deploy innovative AI solutions that help clients solve complex business challenges.
As a Junior AI Engineer, you will contribute to the delivery of enterprise AI, Generative AI, Agentic AI, and data\-driven solutions across a variety of industries. You will gain hands\-on experience with modern AI technologies, cloud platforms, automation tools, and large\-scale enterprise environments.
Responsibilities
- Support the design, development, testing, and deployment of AI and machine learning solutions.
- Assist in building Generative AI and Agentic AI applications using modern AI frameworks and cloud services.
- Develop and maintain data pipelines, integrations, APIs, and automation workflows.
- Collaborate with consultants, solution architects, and client stakeholders to understand business requirements and translate them into technical solutions.
- Configure, fine\-tune, and evaluate AI models to improve performance and business outcomes.
- Participate in data preparation, model training, prompt engineering, and solution validation activities.
- Contribute to technical documentation, implementation guides, and client deliverables.
- Support solution deployment, monitoring, troubleshooting, and performance optimization.
- Stay current with emerging AI technologies, industry trends, and best practices.
- Participate in workshops, demonstrations, and knowledge\-sharing sessions with internal teams and clients.
Qualifications
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Education
- Bachelor's degree in Computer Science, Data Science, Information Technology, Engineering, or a related field.
- MBA preferred.
Required experience
- Open to recent graduates and early\-career professionals with experience in software development, data engineering, AI, or related technical fields gained through academic projects, internships, or professional roles.
- Understanding of machine learning concepts and AI fundamentals.
- Experience with Python and common development frameworks.
- Familiarity with SQL and working with structured and unstructured data.
- Knowledge of REST APIs, Git, and software development best practices.
- Strong analytical, problem\-solving, and communication skills.
- Strong communication and interpersonal skills, with the ability to present ideas clearly and build positive working relationships with team members and stakeholders.
- Ability to work effectively in a fast\-paced, client\-focused consulting environment.
- Ability to travel as required.
Preferred experience
- Exposure to Generative AI technologies such as Azure OpenAI, Anthropic Claude, or similar platforms.
- Familiarity with cloud platforms such as Microsoft Azure, AWS, or Google Cloud.
- Knowledge of data visualization platforms such as Power BI.
- Understanding of MLOps, model deployment, and AI governance concepts.
- Relevant certifications in AI, cloud, or data technologies are a plus.
- Only candidates legally authorized to work for any employer in the U.S on a full\-time basis without the need for sponsorship will be considered. We are unable to sponsor or take over sponsorship of an employment Visa at this time.
Additional Information
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Compensation and Benefits
- Placement within the salary range is based on the candidate’s experience, qualifications, demonstrated capabilities, and alignment with the requirements of the role.
- Bonus eligible
- 401K Savings Plan with Company Match
- Medical / Dental / Vision coverage
- Employee Stock Options Plan (ESOP)
- 25 PTO / 6 Federal Holidays / 4 Floating Holidays
- 4 months parental leave to birthing parent / 2 months to supporting parent
Travel and Location
This full\-time position is based in our Pittsburgh office. You must reside within commuting distance of our North Shore office.
This role is expected to work flexibly based on client and business needs. This role may require periodic travel to client sites and/or attendance at a designated hub office to support project delivery, collaboration, and team engagement.
Diversity and Inclusion
Wavestone is an equal opportunity employer. We embrace diversity as a core component of our culture. Our collective success depends heavily on the recruitment and inclusion of qualified professionals, regardless of individual characteristics such as race, ancestry, religion, color, sex, age, national origin, sexual orientation, gender identity, disability, veteran’s status, or any characteristic protected by law.
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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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Wavestone, 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. Entry-level AI roles across all categories have a median of $110,000.
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.
Wavestone AI Hiring
Wavestone has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Pittsburgh, PA, US.
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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