FAQs
While closely related, AI engineers and ML engineers have distinct focuses. Machine learning engineers specialize deeply in designing, building, training, and optimizing ML models—they spend most of their time on algorithm selection, feature engineering, model evaluation, and data pipeline architecture for tasks like classification, regression, and recommendation systems. AI engineers have a broader scope: they build complete intelligent systems that integrate various AI capabilities including ML models, natural language processing, computer vision, knowledge representation, and reasoning systems. AI engineers focus on system-level architecture, deploying models into production environments, and ensuring AI systems interact effectively with users and other software components. Think of it this way: an ML engineer might build a fraud detection model, while an AI engineer builds the entire fraud prevention system that incorporates that model alongside rule engines, user interfaces, and monitoring systems. AI engineers typically need broader skills across multiple AI domains, while ML engineers go deeper into statistical modeling and algorithm optimization. That said, many roles blur these lines, and both positions work closely together at startups—particularly at early-stage AI companies where everyone wears multiple hats.
AI engineers need strong programming skills in Python (used by 77.4% of AI engineers), plus familiarity with languages like C++, Java, or JavaScript depending on the deployment environment. You should be proficient with deep learning frameworks including TensorFlow and PyTorch for building and training neural networks, plus Keras for rapid prototyping. Increasingly important are LLM frameworks like LangChain, Hugging Face Transformers, and vector databases for building AI applications with large language models. Essential Python libraries include NumPy and Pandas for data manipulation, scikit-learn for traditional ML, and Matplotlib/Seaborn for visualization. You need solid understanding of machine learning fundamentals (supervised/unsupervised learning, model evaluation, overfitting), deep learning architectures (CNNs, RNNs, transformers), and generative AI including GANs and diffusion models. Strong mathematics—linear algebra, calculus, probability, and statistics—underpins effective AI engineering. Familiarity with cloud platforms (AWS, Google Cloud, Azure), data pipeline tools (Apache Spark, Kafka, Airflow), and MLOps practices for model deployment and monitoring is valuable. Equally important are soft skills: strong communication to explain complex AI concepts to non-technical stakeholders, collaborative mindset for working across data science and engineering teams, and commitment to continuous learning given how rapidly AI technology evolves.
Jack and Jill connects AI engineers with diverse roles at the fastest-growing, most promising tech startups and scaleups in London and San Francisco. Many of our clients are major AI companies offering opportunities in foundational AI infrastructure engineering (building LLM platforms, vector databases, ML training systems), applied AI engineering (implementing computer vision, NLP, speech recognition in products), LLM engineering (fine-tuning models, building RAG systems, prompt engineering), generative AI development (creating AI content generation, code assistants, creative tools), MLOps and AI platform engineering (deployment pipelines, model monitoring, scaling inference), and research engineering (translating cutting-edge AI research into production systems). We focus on senior AI engineer positions and founding engineer roles at companies backed by tier-one investors like Sequoia, a16z, and Greylock, though we also work with exceptional mid-level engineers ready for high-impact opportunities. Every role is vetted to ensure strong technical culture, meaningful ownership, competitive compensation packages with significant equity upside, and the chance to work on genuinely novel AI problems rather than integrating third-party APIs. These aren't generic software engineering roles with AI sprinkled in—these are positions where AI engineering is the core product.
Unlike LinkedIn or traditional job boards where you manually sift through hundreds of loosely-defined "AI engineer" roles (many of which are actually data engineering or software engineering positions with minimal AI work), Jack, our AI recruiter, does intelligent matching for you. After a conversation where Jack learns your AI specialization (LLMs, computer vision, NLP, reinforcement learning, etc.), preferred frameworks (PyTorch vs TensorFlow, LangChain experience, etc.), ideal company stage and problem domain, and compensation expectations, he continuously scans 99% of public jobs across 100,000+ career sites (approximately 15,000 new positions hourly) for AI engineering opportunities in London and San Francisco. Jill also works directly with AI companies for off-market roles and direct introductions. Jack understands the nuanced differences between AI engineering roles—he won't send you generic software engineering positions or data science roles when you're looking for hands-on AI model development and deployment. When there's strong mutual fit, you'll be introduced to CTOs, heads of AI, or founding engineers, bypassing application black holes and recruiter spam. The platform is completely free for candidates and eliminates the frustration of applying to "AI engineer" roles that turn out to be standard backend engineering with minimal AI involvement. Many AI engineers receive relevant introductions within 24 hours of their first conversation with Jack.
Getting started is simple: visit jackandjill.ai and join as a job seeker to start chatting with Jack, our AI recruiter. Jack will ask about your AI engineering background, the types of models and systems you've built (computer vision pipelines, NLP applications, LLM-based products, etc.), your preferred frameworks and tools (PyTorch, TensorFlow, LangChain, Hugging Face, etc.), the AI problem domains that excite you most, whether you prefer AI engineer jobs in London or San Francisco, and your compensation expectations. You'll connect your LinkedIn profile and can upload your CV so Jack can better understand your technical depth and project experience. From there, Jack continuously scans 99% of public jobs across 100,000+ career sites while Jill works directly with AI companies and labs on off-market opportunities. When there's strong alignment with companies seeking your specific AI expertise, you'll receive introductions. The entire process is completely free for candidates. Note that by default, your profile is shared with potential employers, but you can disable automatic sharing or blacklist specific companies in your account settings at any time. Many AI engineers find this more efficient than spending hours on job boards or managing outreach from recruiters who don't understand the technical distinctions between different AI engineering specializations, with some receiving relevant introductions to hiring managers or CTOs within the first day.