8 answers
7
Questions
Updated
406 views
skills needed to work in ai?
after looking you need a mix of technical expertise
Login to comment
8 answers
Updated
Manu Nicholas’s Answer
Assuming you mean working on the backend of AI and building tools with it I would say the main langauage a lot of tool work with is Python. Get comfortable with it and learn NumPy, Pandas, PyTorch, and TensorFlow.
Understand machine learning fundamentals before jumping into deep learning. Regression, classification, clustering, how models are trained and evaluated. A lot of people skip this and wonder why nothing makes sense later.
Then go deeper into neural networks, transformers, and whatever area of AI excites you whether that's NLP, computer vision, robotics, or something else. Pick a lane and build projects in it.
Also learn how to read research papers. AI moves fast and the latest stuff hits ArXiv long before it becomes a tutorial.
Also build projects and contribute to Open source repositories on github. these are the best ways to stay ahead of the pack
Understand machine learning fundamentals before jumping into deep learning. Regression, classification, clustering, how models are trained and evaluated. A lot of people skip this and wonder why nothing makes sense later.
Then go deeper into neural networks, transformers, and whatever area of AI excites you whether that's NLP, computer vision, robotics, or something else. Pick a lane and build projects in it.
Also learn how to read research papers. AI moves fast and the latest stuff hits ArXiv long before it becomes a tutorial.
Also build projects and contribute to Open source repositories on github. these are the best ways to stay ahead of the pack
Updated
Anurupa’s Answer
Mathematics & Statistics: Linear algebra, calculus, probability, and optimization theory to build, tune, and debug algorithms.
Programming & Software Engineering: Proficiency in Python (primary language), C++/Rust (for high performance), standard data structures, algorithms, and software design principles.
Data Management: Skills in SQL, vector databases (Pinecone, Chroma), big data tools (Apache Spark), and data processing (Pandas, NumPy).
Machine Learning & Deep Learning: Core algorithms (regression, decision trees), deep neural architectures (Transformers, CNNs), and frameworks like PyTorch and TensorFlow.
Specialized AI Domains: Applied techniques in NLP/Generative AI (LLM fine-tuning, RAG, LangChain), Computer Vision, or Reinforcement Learning.
MLOps & Deployment: Experience with cloud environments (AWS, GCP, Azure), containerization (Docker, Kubernetes), and CI/CD pipeline automation (MLflow).
Soft Skills & Strategy: Business problem formulation, effective communication, data privacy awareness, and AI ethics.
Programming & Software Engineering: Proficiency in Python (primary language), C++/Rust (for high performance), standard data structures, algorithms, and software design principles.
Data Management: Skills in SQL, vector databases (Pinecone, Chroma), big data tools (Apache Spark), and data processing (Pandas, NumPy).
Machine Learning & Deep Learning: Core algorithms (regression, decision trees), deep neural architectures (Transformers, CNNs), and frameworks like PyTorch and TensorFlow.
Specialized AI Domains: Applied techniques in NLP/Generative AI (LLM fine-tuning, RAG, LangChain), Computer Vision, or Reinforcement Learning.
MLOps & Deployment: Experience with cloud environments (AWS, GCP, Azure), containerization (Docker, Kubernetes), and CI/CD pipeline automation (MLflow).
Soft Skills & Strategy: Business problem formulation, effective communication, data privacy awareness, and AI ethics.
Updated
Genesys’s Answer
Here is a mix of answers from Genesys volunteers:
Keep updated. Don't depend on it or outsource your work to AI and allow it to make decisions.
AI is a tool and knowing how to properly use it will carry you far beyond your peers. If you over-rely on it you'll be shortchanging your own development, BUT you also need to know how to build and use AI to your purposes and your career.
Use it as a tool to dig deeper and be curious. Using AI to complete a task is helpful for getting things done, but the real value comes from being able to leverage your knowledge and ask insightful questions.
Keep updated. Don't depend on it or outsource your work to AI and allow it to make decisions.
AI is a tool and knowing how to properly use it will carry you far beyond your peers. If you over-rely on it you'll be shortchanging your own development, BUT you also need to know how to build and use AI to your purposes and your career.
Use it as a tool to dig deeper and be curious. Using AI to complete a task is helpful for getting things done, but the real value comes from being able to leverage your knowledge and ask insightful questions.
Updated
PARTH’s Answer
To work in AI, you need a mix of technical, data, and practical delivery skills:
Programming: Python first, plus SQL for working with data; Git is also table stakes.
ML fundamentals: supervised/unsupervised learning, model training, evaluation, and basics of deep learning.
Data skills: cleaning data, feature engineering, analysis, and understanding pipelines/ETL.
GenAI skills: prompt engineering, LLM basics, RAG, and tools/frameworks like LangChain.
Deployment skills: cloud, APIs, Docker, MLOps, and monitoring models in production.
Soft skills: problem-solving, communication, business understanding, and responsible AI awareness matter a lot in hiring.
Best practical advice: start with Python + SQL + ML basics + 2 real projects. Employers increasingly value hands-on projects more than just theory, so build something usable, not just notebooks.
Programming: Python first, plus SQL for working with data; Git is also table stakes.
ML fundamentals: supervised/unsupervised learning, model training, evaluation, and basics of deep learning.
Data skills: cleaning data, feature engineering, analysis, and understanding pipelines/ETL.
GenAI skills: prompt engineering, LLM basics, RAG, and tools/frameworks like LangChain.
Deployment skills: cloud, APIs, Docker, MLOps, and monitoring models in production.
Soft skills: problem-solving, communication, business understanding, and responsible AI awareness matter a lot in hiring.
Best practical advice: start with Python + SQL + ML basics + 2 real projects. Employers increasingly value hands-on projects more than just theory, so build something usable, not just notebooks.
Updated
Mari’s Answer
To succeed in AI, you need both technical and soft skills. Focus on learning Python, SQL, machine learning, prompt engineering, data handling, and basic cloud and deployment knowledge. Also, work on your communication, critical thinking, adaptability, and ability to explain AI results clearly.
Updated
Manasa’s Answer
1. Understanding the basics of AI and programming is essential. You should be able to read code, grasp its purpose, and assess its efficiency.
2. Mastering prompt engineering is crucial. Learn to ask the right questions to generate code, brainstorm, or handle any software development task effectively.
3. If you can, choose a specific field like insurance or banking or something else to specialize in. This focus will set you apart, and while job options may be fewer than general coding, your chances of getting hired quickly will improve.
2. Mastering prompt engineering is crucial. Learn to ask the right questions to generate code, brainstorm, or handle any software development task effectively.
3. If you can, choose a specific field like insurance or banking or something else to specialize in. This focus will set you apart, and while job options may be fewer than general coding, your chances of getting hired quickly will improve.
Updated
Genesys’s Answer
Here is a mix of answers from Genesys volunteers:
Research how it’s being used in your field by professionals and experiment with tools (if feasible). Think of it as a tool/aide, but don’t be dependant on it.
Use it as a tool to enhance your work, not replace it! Understand how to create prompts that will return the output you're looking for. Utilize message boards to see how others in your industry have found success with AI and practice, practice, practice!
To effectively use AI, simply experiment with it and learn through trial and error. Don’t lose hope or trust in AI, but also critically evaluate everything it does.
Research how it’s being used in your field by professionals and experiment with tools (if feasible). Think of it as a tool/aide, but don’t be dependant on it.
Use it as a tool to enhance your work, not replace it! Understand how to create prompts that will return the output you're looking for. Utilize message boards to see how others in your industry have found success with AI and practice, practice, practice!
To effectively use AI, simply experiment with it and learn through trial and error. Don’t lose hope or trust in AI, but also critically evaluate everything it does.
Updated
Neli’s Answer
Prompt engineering and machine learning are a must in my opinion.