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Applied Ai Engineer Jobs in Riverside, NJ (NOW HIRING)

Nuuly

Nuuly Senior Software Engineer

Philadelphia, PA · On-site +1

$112K - $148K/yr

This is a hands-on engineering role for someone who wants to build the tooling and process infrastructure behind applied AI, not just use AI coding assistants day to day. Role Responsibilities

Nuuly

Nuuly Senior Software Engineer

Philadelphia, PA · On-site +1

$112K - $148K/yr

This is a hands-on engineering role for someone who wants to build the tooling and process infrastructure behind applied AI, not just use AI coding assistants day to day. Role Responsibilities

Nuuly

Nuuly Senior Software Engineer

Philadelphia, PA · On-site

$123K - $163K/yr

This is a hands-on engineering role for someone who wants to build the tooling and process infrastructure behind applied AI, not just use AI coding assistants day to day. Role Responsibilities

Burro

Senior Robotics AI Engineer

Philadelphia, PA

$160K - $180K/yr

We are seeking a Senior Robotics AI Enginee r with strong applied machine learning and robotics ... The ideal candidate will have a strong grasp of robotics and software engineering fundamentals ...

Burro

Senior Robotics AI Engineer

Philadelphia, PA · On-site

$160K - $180K/yr

We are seeking a Senior Robotics AI Enginee r with strong applied machine learning and robotics ... The ideal candidate will have a strong grasp of robotics and software engineering fundamentals ...

USCS

AI Platform Engineer

Camden, NJ · On-site

$140K - $150K/yr

Applied AI experience. At least 3 years of hands-on experience developing and delivering Generative AI solutions, with familiarity in large language models, prompt engineering, RAG, embeddings ...

Worky

AI Platform Engineer

Conshohocken, PA · On-site

$140 - $150/hr

Applied AI experience. At least 3 years of hands-on experience developing and delivering Generative AI solutions, with familiarity in large language models, prompt engineering, RAG, embeddings ...

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Applied Ai Engineer information

What are the key skills and qualifications needed to thrive as an applied AI engineer?

To thrive as an Applied AI Engineer, you need strong proficiency in programming (especially Python), machine learning algorithms, statistics, and a relevant degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with cloud platforms (such as AWS or Azure), and knowledge of data management tools are typically required. Excellent problem-solving, communication, and teamwork skills help you translate complex models into real-world solutions and collaborate across disciplines. These competencies ensure you can effectively develop, deploy, and maintain AI systems that drive business value.

What are some common challenges applied AI engineers face when deploying AI models into production environments?

Applied AI Engineers often encounter challenges such as ensuring models perform consistently on real-world data, optimizing models for speed and scalability, and integrating AI solutions with existing systems. Managing data privacy, monitoring for model drift, and maintaining robust documentation are also key concerns. Collaboration with DevOps, data engineering, and product teams is essential to address these challenges effectively and deliver reliable AI-driven solutions.

What is the difference between Applied Ai Engineer vs Data Scientist?

AspectApplied Ai EngineerData Scientist
Required CredentialsBachelor's or Master's in CS, AI, or related fields; experience with AI frameworksBachelor's or Master's in CS, Statistics, or related fields; strong analytical skills
Work EnvironmentDevelops and deploys AI models in production environmentsAnalyzes data to extract insights and build predictive models
Industry UsageUsed in tech, healthcare, finance for deploying AI solutionsUsed across industries for data analysis and modeling

Applied Ai Engineers focus on implementing and deploying AI models in real-world applications, while Data Scientists primarily analyze data to generate insights and build predictive models. Both roles require similar educational backgrounds but differ in their core responsibilities and work environments.

How much does an applied AI engineer make?

An applied AI engineer's salary varies based on experience, location, and industry, but typically ranges from $80,000 to $150,000 annually. Senior roles or those with specialized skills in machine learning, deep learning, and programming languages like Python or TensorFlow tend to earn higher salaries.

What does an applied AI engineer do?

An applied AI engineer develops and implements artificial intelligence models and algorithms to solve real-world problems. They work with data, machine learning frameworks, and programming languages like Python or TensorFlow to create practical AI solutions for businesses or products.

What cities near Riverside, NJ are hiring for Applied Ai Engineer jobs?

Cities near Riverside, NJ with the most Applied Ai Engineer job openings:

Applied Ai Engineer jobs near you

Principal Machine Learning Engineer

Delan Associates, Inc

Philadelphia, PA • On-site

Full-time

Re-posted 4 days ago

Job description

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.

Hands-On Model Development

Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.

Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.

Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.

Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.

Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.

Move quickly from data exploration to prototype to validated model to production-ready capability.

Required Qualifications

Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.

5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.

3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.

Strong hands-on experience with Python and SQL.

Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.

Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.

Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.

Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.

Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.

Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.

Scoring, Scorecards, and Transparent Models

Production ML and MLOps

Product and Rapid-Build Execution

Generative AI and AI Automation

Requirement Shaping and Stakeholder Partnership