Careers in AI - Job Opportunities and Complete Descriptions

Data Scientists analyze complex data to help organizations make informed decisions. They use statistical and machine learning techniques to extract insights from data, create predictive models, and identify trends and patterns.

Understanding of mathematical concepts.

Proficiency in Python, R, and SQL.

Ability to create visual representations of data.

Knowledge of algorithms and frameworks.

Cleaning and organizing raw data.

Building predictive models.

Interpreting data trends and patterns.

Visualizing data using tools

Communicating insights to stakeholders.

Google

Facebook

Amazon

IBM

Airbnb

Netflix

Coursera: Data Science Specialization

edX: Data Science MicroMasters Program

Udacity: Data Scientist Nanodegree

Pluralsight: Data Science with Python

• Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

• Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

• The Data Science Handbook

Machine Learning Engineers design and implement machine learning models to solve specific problems. They work closely with data scientists to develop algorithms that can learn from and make predictions based on data.

Python, Java, C++.c

TensorFlow, PyTorch, Keras.

Strong understanding of ML algorithms and data structures.c

Linear algebra, calculus, probability, and statistics.

Developing and training machine learning models.

Implementing and optimizing algorithms for better performance.

Deploying machine learning models into production environments.

Apple

Microsoft

NVIDIA

Intel

Tesla

Amazon

edX: Professional Certificate in Machine Learning and Artificial Intelligence

Coursera: Machine Learning by Stanford University

Udacity: Machine Learning Engineer Nanodegree

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

• Pattern Recognition and Machine Learning

• Machine Learning Yearning

AI Research Scientists conduct research to advance the field of artificial intelligence. They develop new algorithms, models, and methodologies to solve complex problems.

Expertise in neural networks and deep learning frameworks.

Python, R.c

Knowledge of statistics, linear algebra, and calculus.

Ability to publish research papers and present findings.

Developing new AI algorithms and models.

Conducting experiments to test hypotheses and validate models.

Publishing research papers and presenting findings.

Collaborating with academic partners on research projects.

DeepMind

OpenAI

IBM Research

Google Research,

Meta AI Research

Microsoft Research

Udacity: Deep Learning Nanodegree

Coursera: AI For Everyone by Andrew Ng

edX: Artificial Intelligence MicroMasters Program

Pluralsight: Deep Learning: The Big Picture

• Artificial Intelligence: A Modern Approach

• Deep Learning

• Reinforcement Learning: An Introduction

Data Engineers build and maintain data infrastructure and pipelines. They ensure that data is collected, stored, and processed efficiently, making it accessible for data scientists and analysts.

SQL, Python, Java.

Expertise in Extract, Transform, Load (ETL) processes.

Knowledge of data warehousing solutions

Familiarity with Hadoop, Spark.

Designing and implementing data pipelines to collect and process data.

Ensuring data quality and reliability through regular maintenance and optimization.

Managing data storage solutions and ensuring efficient data retrieval.

Netflix

Spotify

LinkedIn

Uber

Google

Amazon

Coursera: Big Data Specialization

edX: Professional Certificate in Data Engineering

Udacity: Data Engineer Nanodegree

Pluralsight: Data Engineering

• Data Engineering on Azure

• Designing Data-Intensive Applications

• Data Pipelines Pocket Reference

AI Product Managers oversee the development and implementation of AI products. They work at the intersection of business, technology, and user experience, ensuring that AI products meet market needs and deliver value.

Experience in managing tech projects.

Understanding of AI and machine learning concepts.

Ability to align AI products with business goals.

Strong communication skills

Defining product requirements based on market research and user needs.

Coordinating with engineering and design teams to ensure timely development.

Managing project timelines

Conducting market research

Google

Amazon

Salesforce

IBM

Adobe

Microsoft

edX: AI Product Management Program

Coursera: AI For Everyone by Andrew Ng

Udacity: Product Manager Nanodegree

Pluralsight: AI Product Management

• Lean AI: How Innovative Startups Use Artificial Intelligence to Grow

• AI Superpowers: China, Silicon Valley, and the New World Order

• Product Management's Sacred Seven

AI Ethicists focus on the ethical implications and societal impact of AI. They assess the ethical aspects of AI projects, develop guidelines and policies for ethical AI use, and conduct ethical reviews and impact assessments.

Strong understanding of ethical theories and principles.

Familiarity with AI technologies

Ability to conduct ethical reviews and impact assessments.

Excellent written and verbal communication skills.

Assessing the ethical implications of AI projects and technologies.

Developing guidelines and policies for ethical AI use.

Conducting ethical reviews and impact assessments for AI initiatives.

Google

Microsoft

IBM

Accenture

PwC

Deloitte

edX: Ethics in AI

Coursera: AI Ethics and Society

Udacity: AI Ethics Nanodegree

Pluralsight: Ethics and AI

• Ethics of Artificial Intelligence and Robotics

• Artificial Unintelligence: How Computers Misunderstand the World

• The Ethical Algorithm: The Science of Socially Aware Algorithm Design

AI Consultants provide expert advice on AI strategy, implementation, and optimization. They work with clients to identify AI opportunities, recommend solutions, and assist with implementation to drive business value.

In-depth understanding of AI technologies.

Ability to align AI solutions with business objectives.c

Strong presentation and communication skills.

Analytical and problem-solving abilities.

Assessing client needs and identifying AI opportunities.

Recommending AI solutions and strategies.

Assisting with the implementation and optimization of AI systems.

Providing training and support to client teams on AI technologies.

Deloitte

Accenture

BCG

McKinsey

Capgemini

PwC

Coursera: AI for Everyone

edX: Artificial Intelligence: Business Strategies and Applications

Udacity: AI for Business Leaders Nanodegree

Pluralsight: AI: Getting Started

• The AI-First Company: How to Compete and Win with Artificial Intelligence

• Prediction Machines: The Simple Economics of Artificial Intelligence

• AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

NLP Engineers develop systems that enable computers to understand, interpret, and respond to human language. They work on tasks such as text analysis, machine translation, sentiment analysis, and chatbots.

Proficiency in Python, Java.

Experience with NLP libraries like NLTK, spaCy.

Understanding of ML algorithms.

Knowledge of linguistic concepts and structures.

Building and optimizing NLP models for various processing tasks.

Developing interactive chatbots and virtual assistants.

Implementing text analysis and summarization systems.

Conducting sentiment analysis

Google

Amazon

Facebook

Microsoft

Apple

OpenAI

edX: Natural Language Processing with Python

Coursera: Natural Language Processing Specialization

Udacity: Natural Language Processing Nanodegree

Pluralsight: NLP with Python

• Natural Language Processing with Python

• Speech and Language Processing

• Deep Learning for Natural Language Processing

AI Hardware Specialists focus on developing and optimizing hardware to run AI algorithms efficiently. They work on designing AI accelerators, optimizing hardware for AI workloads, and collaborating with software engineers to improve performance.

Understanding of computer architecture and hardware design.

Proficiency in hardware description languages like VHDL, Verilog.

Familiarity with AI and machine learning concepts.

Strong analytical skills.

Designing AI accelerators and specialized hardware for AI applications.

Optimizing hardware for efficient execution of AI algorithms.

Collaborating with software engineers to integrate and optimize AI hardware.

NVIDIA

Intel

AMD

Google

Apple

Microsoft

Coursera: AI For Hardware Design

edX: Computer Architecture

Udacity: Self-Driving Car Engineer Nanodegree

Pluralsight: Digital Systems: From Logic Gates to Processors

• AI Hardware Architectures and Engineering

• Artificial Intelligence Hardware Design: Challenges and Solutions

• Fundamentals of Digital Logic with VHDL Design

Robotics Engineers design, build, and maintain robots that perform tasks autonomously or semi-autonomously. They work on developing robotic systems, creating control algorithms, and integrating sensors and actuators.

Knowledge of mechanics and electronics.

Proficiency in C++, Python, ROS (Robot Operating System).

Understanding of control theory and automation.

Familiarity with machine learning and computer vision.

Designing robotic systems and prototypes.

Developing control algorithms for robot movement and behavior.

Integrating sensors and actuators into robotic systems.

Troubleshooting robotic systems to ensure proper functionality.

Boston Dynamics

iRobot

ABB

Amazon Robotics

FANUC

KUKA

Udacity: Robotics Software Engineer Nanodegree

Coursera: Modern Robotics Specialization

edX: Robotics MicroMasters Program

Pluralsight: Introduction to Robotics

• Robotics: Everything You Need to Know About Robotics from Beginner to Expert

• Introduction to Robotics: Mechanics and Control

• Modern Robotics: Mechanics, Planning, and Control