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About This Role
Role: ML/AI Engineers
(This role is open to US Citizens, Green Card holders, GC\-EAD only. We do not sponsor visas.)
Summary:
Adidev is looking for an adept Machine Learning Engineer to take the helm in deploying advanced machine learning models, with a special emphasis on Generative AI. In this role, you will craft and refine AI\-driven solutions, turning innovative ideas into value\-adding features and services, thereby solidifying our market leadership and technological forefront for our clients.
About Adidev Technologies Inc.
Adidev Technologies,(www.adidevtechnologies.com) is a premier IT consulting firm delivering top\-notch, Machine Learning Engineer, iOS and Android, data scientist, Developer solutions to industry giants including Delta, Google, Apple, Spotify, US Bank, FedEx, and more. We're not just a software consulting company – we're a dynamic force shaping the future of technology. Partnering with industry giants, we consistently deliver groundbreaking solutions that redefine the digital landscape. As we continue to expand our footprint, we're on the hunt for exceptional individuals who can bring their technical prowess to our team and elevate our projects to new levels of innovation.
Expertise: We excel in IT consultative services and quality engineer development, with Good years of experience.
Global Presence: Our diverse employee workforce spans four continents.
Proven Track Record: Hundreds of Fortune 1000 and innovative startup clients with thousands of successful projects across the USA.
How We'll Guide You
Teaching and Development: We are dedicated to nurturing your growth and development, shaping you into an exceptional consultant capable of delivering top\-tier solutions to our end clients.
Custom Support: An array of teams, from Development Managers to Tech Subject Matter Experts, are dedicated to your success.
Project Placement: A market\-expertise team ensures you secure and thrive in projects with our esteemed clients.
Career Growth: We facilitate industry experience to propel your technical journey forward.
Key Responsibilities:
- Architect and refine sophisticated ML models and algorithms, translating complex datasets into actionable solutions.
- Engage in the full lifecycle of data modeling projects, from understanding business requirements to deployment and monitoring.
- Execute comprehensive data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions.
- Lead cross\-functional collaborations to integrate Generative AI models into our offerings, enhancing product capabilities and user experiences.
- Apply advanced analytical techniques to analyze vast datasets, identifying trends, anomalies, and opportunities for improvement.
- Execute data preprocessing, feature engineering, and algorithm optimization to enhance model accuracy and efficiency.
- Conduct exploratory data analysis to extract valuable insights and influence strategic decisions.
- Keep abreast of and implement the latest ML trends, tools, and best practices, including AutoML, MLOps, and interpretability frameworks.
- Promote compliance with industry standards and regulatory requirements, emphasizing ethical AI practices.
Requirements:
- Degree in Computer Science, Engineering, Statistics, or a related technical field.
- Demonstrable experience in machine learning, deep learning, NLP, computer vision, reinforcement learning, and/or other AI domains.
- Demonstrable experience with Generative AI models and frameworks, such as GANs or Transformers, applied in industry settings.
- Practical experience with SQL/NoSQL databases, data visualization tools, and version control systems.
- Strong foundational understanding of algorithmic complexity and data structure optimization.
- Excellent problem\-solving, collaboration, and communication abilities.
- Develop and implement cutting\-edge machine learning models, with a particular focus on Generative AI applications such as text generation, image synthesis, and creative AI.
- Execute comprehensive data analysis, including preprocessing, feature engineering, and leveraging Generative AI algorithms for novel solutions.
- Stay ahead of AI research, especially in Generative AI, applying the latest findings and techniques to drive innovation within our projects.
- Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit\-learn).
- Strong background in cloud computing and big data platforms (AWS, Azure, GCP), with hands\-on experience in cloud\-based ML services and serverless architectures.
- Familiarity with DevOps for AI, including containerization (Docker, Kubernetes), CI/CD pipelines, and MLOps practices.
- Facilitate knowledge sharing and best practices in AI/ML, particularly focusing on Generative AI, within the team.
- Ensure all AI implementations are compliant with ethical guidelines and data privacy standards.
How to Apply:
Interested candidates are invited to submit a comprehensive application, including a latest updated resume and a detailed cover letter, showcasing your expertise.
Perks and Beyond!
Competitive salary range: Based on experience and market value
Pack your bags! Paid relocation is on us.
Support, even from afar, with our remote assistance.
Regular salary reviews? You betcha!
Ready to Embark?
we invite you to take this extraordinary step with us. Showcase your journey in pushing the limits of mobile engineering by submitting your resume and a curated selection of your most influential projects. At Adidev Technologies, we're dedicated to shaping your success. Join us to craft a future powered by innovation and growth.
Note: Adidev Technologies Inc. is a staunch advocate of diversity and equal opportunity. We warmly welcome applications from candidates of all backgrounds, experiences, and walks of life. Your unique perspective could be the catalyst for our next revolutionary breakthrough
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Adidev Technologies Inc, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Adidev Technologies Inc AI Hiring
Adidev Technologies Inc has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Edison, NJ, US, Austin, TX, US, Alpharetta, GA, US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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