How do I hire for a senior AI development team?

Read Time 11 mins | Written by: Cole

We’ve talked with more and more technology leaders who find themselves tasked with building AI software development teams. They need to hire senior software engineers, recruit experts in new technology, and fit their new teams within existing budgets. Not an easy or small challenge. Where do you start?

With new AI technology comes new roles on software development teams. Your team will include some classic roles but need to account for advanced machine learning, data science, and AI architecture. You’ll need senior software engineers in each of these positions – ones that know LLMs, RAG, Python, PyTorch and the latest in AI technologies.

Let’s start with looking at the key roles you’ll need to hire for.

Key roles on an AI software development team

team will depend on your specific projects, industry, and organizational structure. You might not need all these roles from the start, and in smaller teams, individuals may wear multiple hats.
Here are the core roles you need to consider.

1. Machine learning engineer
Responsibilities: Developing and deploying ML models, optimizing model performance, implementing MLOps practices
Skills: Strong programming skills (Python, Java, C++), experience with ML frameworks (TensorFlow, PyTorch), understanding of distributed computing
Salary Range: $90,000 - $200,000+

2. Data scientist
Responsibilities: Analyzing complex datasets, developing predictive models, communicating insights to stakeholders
Skills: Statistical analysis, data visualization, machine learning algorithms, domain expertise
Salary Range: $85,000 - $180,000+

3. AI research scientist
Responsibilities: Conducting cutting-edge AI research, developing novel algorithms, publishing findings in academic journals
Skills: PhD in AI, ML, or related field, strong mathematical background, expertise in specific AI domains (NLP, computer vision, etc.)
Salary Range: $110,000 - $250,000+

4. Data engineer
Responsibilities: Designing and maintaining data pipelines, ensuring data quality and accessibility, optimizing data infrastructure
Skills: Database management, ETL processes, big data technologies (Hadoop, Spark), cloud platforms (AWS, GCP, Azure)
Salary Range: $80,000 - $170,000+

5. AI product manager
Responsibilities: Defining AI product strategy, prioritizing AI features and projects, collaborating with cross-functional teams
Skills: Technical background, product management experience, understanding of AI capabilities and limitations
Salary Range: $100,000 - $190,000+

6. AI safety specialist
Responsibilities: Developing ethical AI guidelines, assessing AI systems for bias and fairness, ensuring regulatory compliance
Skills: Background in ethics, law, or philosophy, understanding of AI technologies, strong communication skills
Salary Range: $90,000 - $180,000+

7. UI/UX designer (AI focus)
Responsibilities: Designing intuitive interfaces for AI products, creating data visualizations, conducting user research for AI interactions
Skills: UX design principles, data visualization techniques, understanding of AI capabilities
Salary Range: $75,000 - $160,000+

8. DevOps engineer (MLOps)
Responsibilities: Implementing CI/CD for ML models, managing cloud infrastructure for AI, optimizing model deployment processes
Skills: Cloud platforms, containerization (Docker, Kubernetes), CI/CD tools, understanding of ML workflows
Salary Range: $90,000 - $180,000+

9. AI architect
Responsibilities: Designing overall AI system architecture, aligning AI solutions with business objectives, guiding technology choices and integration strategies, ensuring scalability and performance of AI systems
Skills: Strong background in AI/ML, software architecture principles, cloud computing, system integration, strategic planning, cross-functional communication
Salary Range: $120,000 - $250,000+

Technical and non-technical skills your AI team needs

When building an AI team, it's important to consider not just the individual roles, but the skills each person brings to the table. A well-rounded team with complementary skills can drive innovation and deliver impactful AI solutions.

Technical AI skills

Large language models (LLMs) expertise is crucial for working with cutting-edge AI models like GPT-4, Claude 3.5, and Llama 3. This includes fine-tuning and deploying them for various applications and understanding their underlying architectures and training.
Retrieval-augmented generation (RAG) skills are important for enhancing LLM performance by integrating retrieval systems with generative models. RAG Improves contextual understanding and response generation to increase LLM accuracy.
Deep learning framework proficiency, particularly with TensorFlow and PyTorch, is essential for building complex models, as well as optimizing and fine-tuning them for specific tasks.
NLP libraries and framework knowledge, especially advanced frameworks like Hugging Face transformers and pre-trained language models such as BERT and GPT is vital for natural language processing tasks.
Computer vision libraries and framework expertise – including advanced knowledge of OpenCV, TensorFlow, and PyTorch for image and video analysis – is crucial for visual AI applications.
Big data processing skills, particularly with frameworks like Apache Spark and Apache Flink – are necessary for handling and processing large-scale datasets efficiently in AI applications.
Security and compliance knowledge, including understanding of security protocols, best practices for AI systems, and experience with compliance standards like GDPR and HIPAA – is essential for responsible AI development and deployment.

Non-technical AI skills

Communication skills are vital for collaborating with diverse teams and explaining complex AI concepts to non-technical stakeholders.
Leadership and mentorship abilities help in guiding junior team members and fostering a culture of continuous learning, which is crucial in the rapidly evolving field of AI.
Problem-solving and critical thinking are essential for tackling the complex challenges often encountered in AI development.
Adaptability and continuous learning are particularly important in AI, given the field's rapid pace of advancement.
Empathy and emotional intelligence help in understanding user needs and collaborating effectively in diverse teams.
Business understanding ensures that AI solutions align with organizational goals and deliver real value.
Project management skills are crucial for delivering AI projects on time and within scope, especially given the experimental nature of many AI initiatives.
Attention to detail and a focus on quality are essential for developing robust, reliable AI systems. Creativity and innovation drive the development of novel AI solutions and applications.
Ethics and integrity are particularly crucial in AI development, given the potential societal impacts of AI systems.

Key AI technologies, tools, and programming languages

There’s a long list of new technologies, platforms, and tools your developers need to upskill on to build enterprise AI solutions.

AI cloud platforms

Azure AI: Use a single AI platform to build, evaluate, and deploy generative AI solutions and custom copilots.
AWS Bedrock: Fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Mistral AI, Stability AI, and Amazon via a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
Google Cloud Vertex AI: Deliver generative AI powered experiences quickly, efficiently, and responsibly, powered by Google’s most advanced technology and models including Gemini.
Nvidia DGX Cloud: NVIDIA DGX Platform incorporates the best of NVIDIA software, infrastructure, and expertise in a modern, unified AI development and training solution.

AI programming languages and core libraries

Python: Primary language for AI/ML
NumPy, Pandas: Data manipulation and analysis
Matplotlib, Seaborn: Data visualization
PyTorch: PyTorch enables developers and researchers to build LLMs and modern AI.
R: Statistical computing and data analysis
Java/Scala: Large-scale applications, distributed computing
C++: High-performance AI, robotics
JavaScript: Web-based AI applications
Julia: High-performance numerical computing
Mojo: High-performance programming language for AI development. Combines Python-like syntax with systems programming capabilities. Designed for AI and machine learning workloads. Offers improved performance and memory safety compared to Python
CUDA (Compute Unified Device Architecture): Parallel computing platform and programming model for NVIDIA GPUs. Critical for GPU-accelerated machine learning and deep learning. Used in conjunction with other languages (primarily C++ and Python) to accelerate computations

Large language models (LLMs)

Click here for the most up-to-date list of LLMs. Every time we write an article a new model (or 5) is released that outperforms others.

GPT-4 Turbo & GPT-4o
Claude 3.5 Sonnet
Claude 3 Model family
Llama 3
Gemini Pro
Mistral Large
Command R+
Nemotron

LLM developer production tools

Vellum.ai: Streamlines AI application deployment and scaling, focusing on infrastructure management and optimization.

LLM guardrails

Nvidia NeMo: NeMo Guardrails is an open-source toolkit for easily adding programmable guardrails to LLM-based conversational systems.

AI development environments

Jupyter Notebooks: Web application for creating and sharing documents containing live code, equations, visualizations, and narrative text
VS Code: Source code editor with Python and ML extensions
PyCharm: Integrated development environment for Python

Machine learning frameworks

TensorFlow: Open-source library for numerical computation and large-scale machine learning
PyTorch: Open-source machine learning library known for its flexibility
Scikit-learn: Simple and efficient tools for data mining and data analysis
Keras: High-level neural networks API, running on top of TensorFlow
FastAI: Library that simplifies training fast and accurate neural nets
XGBoost and LightGBM: Gradient boosting frameworks known for their speed and performance

Deep learning and neural networks

TensorFlow Keras: High-level API of TensorFlow 2.0
PyTorch Lightning: Lightweight PyTorch wrapper for high-performance AI research
Fastai: Layered API for PyTorch
Caffe: Deep learning framework made with expression, speed, and modularity in mind

Natural language processing (NLP)

Hugging Face Transformers: State-of-the-art natural language processing
NLTK: Suite of libraries and programs for symbolic and statistical natural language processing
spaCy: Industrial-strength natural language processing
Gensim: Topic modeling, document indexing and similarity retrieval with large corpora
Stanford CoreNLP: Suite of human language technology tools

Computer vision

OpenCV: Computer vision and machine learning software library
TensorFlow Object Detection API: Framework for building computer vision models
PyTorch Vision: Datasets, transforms and models specific to computer vision
Dlib: Toolkit containing machine learning algorithms and tools for creating complex software

Big data processing

Apache Spark: Unified analytics engine for large-scale data processing
Apache Hadoop: Framework for distributed storage and processing of big data
Apache Flink: Stream processing framework for distributed, high-performing, always-available applications
Dask: Flexible library for parallel computing in Python

MLOps and DevOps

Docker: Platform for developing, shipping, and running applications in containers
Kubernetes: Container orchestration system for automating application deployment, scaling, and management
MLflow: Open source platform for the machine learning lifecycle
Kubeflow: Machine learning toolkit for Kubernetes
Weights & Biases: Developer tools for machine learning
DVC (Data Version Control): Version control system for machine learning projects

Internal hiring vs. outsourcing AI software engineers

When building an AI software development team, you have two primary options – internal hiring and outsourcing. Technology leaders usually pay big consultancies millions for slow, low-quality work when they outsource software development. Or they go through the long, expensive process of hiring internally.

Internal Hiring

Control and alignment – Hiring internally gives you more control over the team's work and ensures alignment with your company’s culture and values.
Long-term investment – Building an in-house team is a long-term investment in your company’s capabilities, fostering deeper knowledge and expertise within the organization.
Seamless communication – Internal teams often benefit from more seamless communication and collaboration, as they are fully integrated into the company's processes.

Outsourcing

Access to expertise – Outsourcing allows you to tap into specialized expertise and experience that may not be available in-house. Many outsourcing firms have teams of skilled AI professionals with diverse backgrounds.
Cost-effective – Outsourcing can be more cost-effective, especially for short-term projects or when scaling up quickly. You avoid the overhead costs associated with hiring and maintaining a full-time team.
Flexibility and scalability – Outsourcing offers flexibility, allowing you to scale the team up or down based on project needs without the long-term commitment of full-time hires.
Focus on core activities – By outsourcing non-core activities, your internal team can focus on strategic tasks and core business activities, improving overall efficiency.