AI/ML Engineer
- MM IT
91 Days Ago
NA
NA
Weehawken-NJ
5-44 Years
Required Skills: azure, databrick,ML, machine
Job Description
Position Title: AI/ML Engineer
Location: Weehawken, NJ – Hybrid 3 days onsite role – Locals needed
Interview Mode: Video Call
Must-Have skills:
Azure ML Studio
Azure Databricks
Python
Spark (SQL/Scala/Python) or Azure Data Factory
Responsibilities:
Azure AI and Machine Learning Engineers focus on developing and deploying AI and machine learning models using Azure Machine Learning service and other cognitive services.
They work with data scientists to operationalize machine learning workflows and integrate predictive analytics into applications.
Their role is to ensure that AI solutions are scalable, performant, and integrated with other Azure data services.
These engineers are vital in organizations looking to leverage AI for enhanced decision-making, automation, and creating intelligent applications.
Proven track record as an AI Engineer, with portfolio examples of successful machine learning or AI projects.
Proficiency in programming languages commonly used in AI, such as Python, R, or Java, and experience with ML frameworks like TensorFlow or PyTorch.
Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects.
Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks.
Hands-on experience with Natural Language Processing (NLP), computer vision, or other AI subfields.
Solid grasp of mathematical concepts relevant to AI, such as linear algebra, calculus, and statistics.
Experience with version control systems like Git, enabling effective collaboration and code management Managing available resources such as hardware, data, and personnel so that deadlines are met Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
