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The AI boom is undeniable, with businesses across the globe rapidly integrating artificial intelligence into their operations. Many professionals are looking to tap into this lucrative field, but the variety of AI career options and required skills can be overwhelming. In this post, we’ll break down both technical and non-technical roles in AI, the key skills required for each, and the potential salaries. Whether you’re a seasoned tech expert or someone looking to pivot into AI, there’s a role for you in this booming industry.

Technical Roles in AI

AI is a highly technical field, but it also requires strong communication and problem-solving skills. Let’s start with the top technical roles.

1. Data Scientist

The role of a data scientist is often described as extracting insights from raw data — essentially, “finding butter from buttermilk.” You’ll work with large data sets and use descriptive analytics or train machine learning models to derive actionable insights.

Skills Required:

• Tool Skills: Strong proficiency in programming languages like Python or R, as well as knowledge of machine learning algorithms.
• Core Skills: A solid foundation in statistics and mathematics, along with effective communication skills to convey insights to business stakeholders.

Salaries: Data scientists can expect competitive salaries. The salary range depends on factors such as your experience, location, and the company you work for.

Resources: If you want to learn data science for free, there are several excellent resources available on YouTube, including a detailed week-by-week study plan.

2. AI Engineer (or ML Engineer)

AI engineers are a hybrid between data scientists and software engineers. They build and train machine learning models and deploy them into production, integrating them with the company’s software systems.

Skills Required:

• Tool Skills: Knowledge of machine learning algorithms, Python, and cloud platforms like AWS and Google Cloud.
• Core Skills: Software engineering expertise to deploy and manage machine learning models at scale.

Salaries: AI engineers typically earn similar salaries to data scientists. However, factors like company type, location, and experience level play a significant role in determining compensation.

Resources: There are free resources online to learn how to become an AI Engineer, and many professionals have successfully transitioned into AI roles after following structured learning paths.

3. NLP Engineer (Natural Language Processing)

NLP Engineers specialize in enabling machines to understand and process human language. This could involve anything from creating chatbots to advanced sentiment analysis.

Skills Required:

• Tool Skills: Strong knowledge of NLP libraries such as SpaCy and NLTK.
• Core Skills: Deep understanding of language structures and algorithms used in NLP.

Salaries: Similar to AI engineers, NLP engineers enjoy competitive salaries, especially in research-intensive roles.

4. Computer Vision Engineer

Computer vision engineers work on AI systems that interpret and understand visual data (images and videos). This role is widely used in industries such as healthcare, autonomous vehicles, and security.

Skills Required:

• Tool Skills: Expertise in libraries like OpenCV, YOLO, or TensorFlow.
• Core Skills: Knowledge of image processing, neural networks, and deep learning techniques.

Salaries: The salary range is generally similar to that of AI engineers and NLP engineers, although companies may require advanced qualifications, such as a Ph.D. for specialized roles.

5. MLOps Engineer

MLOps engineers focus on the deployment and management of machine learning models in production. Much like DevOps engineers in software development, they ensure that machine learning models are scalable, reliable, and maintainable.

Skills Required:

• Tool Skills: Familiarity with cloud platforms, Docker, Kubernetes, MLflow, and CI/CD pipelines for machine learning projects.
• Core Skills: Strong understanding of machine learning principles and software engineering practices.

Salaries: MLOps engineers can earn competitive salaries, especially as the demand for scalable AI solutions grows in tech companies.

Non-Technical Roles in AI

Not all AI-related careers require deep technical expertise. Several non-technical roles play an equally important role in the AI ecosystem.

1. AI Product Manager

AI product managers are responsible for guiding the development of AI products, from concept to launch. They work closely with AI engineers, data scientists, and business stakeholders to define the product’s direction and ensure its success in the market.

Skills Required:

• Tool Skills: Proficiency in product management tools such as Jira, Figma, and product roadmapping software like Roadmunk.
• Core Skills: Strong business acumen, stakeholder management, and communication skills.

Salaries: AI product managers are among the highest-paid in the AI space, with salaries for top positions exceeding $900,000 per year in companies like Netflix.

2. AI Ethicist

As AI continues to evolve, the ethical implications of its use are becoming more important. AI ethicists guide teams on how to build AI systems responsibly, ensuring that privacy, bias, and fairness are properly addressed.

Skills Required:

• Core Skills: Background in law or regulatory compliance is highly beneficial. Ethical reasoning and the ability to stay updated on government regulations regarding AI.

Salaries: While relatively new, AI ethics positions are starting to gain traction, with competitive salaries in both the U.S. and India.

3. AI Sales Executive

AI sales executives bridge the gap between AI technology and the clients who need it. They sell AI-driven products and services, leveraging their existing sales experience while gaining knowledge about AI and machine learning concepts.

Skills Required:

• Tool Skills: Familiarity with proposal tools (e.g., DocuSign), CRM software (e.g., Salesforce, HubSpot), and contract management tools.
• Core Skills: Strong sales and communication skills, coupled with an understanding of AI technology.

Salaries: AI sales executives often receive a base salary plus commission, with top earners potentially making more than $350,000 annually, depending on performance and location.

Deciding Which AI Career Path is Right for You

Choosing the right AI career depends on several factors, including your current skills, interests, and long-term goals. Whether you’re more inclined toward coding and building models, or prefer the strategic and business side of AI, there’s a path for you.

Conclusion

AI is an exciting field with tremendous opportunities across a range of roles. Whether you’re a coder, a strategist, or a sales professional, there’s a place for you in the AI landscape. As the demand for AI talent grows, now is the perfect time to upskill and position yourself for success. Start by exploring the resources and tools mentioned in this post, and take the first step towards a rewarding AI career!