What Does a AI / Machine Learning Engineer Actually Do?
An AI/ML Engineer sits between research and production, building the infrastructure and pipelines that take machine learning models from a notebook into real applications at scale. Day-to-day work involves developing training pipelines, optimising model inference, integrating LLM APIs like OpenAI or Anthropic, managing experiment tracking with MLflow or Weights & Biases, and working with data scientists to productionise their work. You'll work at AI-native startups, large tech companies, and increasingly at banks, retailers, and healthcare firms building AI products. You typically report to a Head of Engineering or VP of AI.
Raj Patel
Machine Learning Engineer
📍 London, UK✉️ raj.patel@email.com
Summary
Machine Learning Engineer with 5 years of experience building and deploying production ML systems. Expert in LLM fine-tuning, computer vision, and MLOps. Experience at both start-ups and FAANG-scale companies.
Work Experience
ML Engineer at DeepMindApr 2023 — Present
Design and train large language models for scientific research applications
Optimise training pipelines reducing GPU compute costs by 30% through mixed-precision training
ML Engineer at RevolutJun 2020 — Mar 2023
Built real-time fraud detection system processing 15M+ transactions daily with 50ms latency
Deployed computer vision KYC pipeline reducing identity verification time from 24 hours to 30 seconds
ML Engineer CVs must show production deployment experience, not just research. Recruiters want to see models you deployed at scale, the infrastructure you built, and measurable business outcomes. Publications and open-source contributions add credibility.
Listing every ML technique without showing real-world application. Focus on 3 to 5 production systems you built and their business impact. Show latency, throughput, and cost optimisation achievements.
Formatting Tips
One to two pages. Include a Selected Projects section if your work spans multiple domains. Link to publications, GitHub, or demo projects prominently.
Average Salary — AI / Machine Learning Engineer
United States
$120,000 – $175,000
United Kingdom
$75,000 – $115,000
Germany
$75,000 – $115,000
UAE / Dubai
$80,000 – $125,000
Canada
$95,000 – $140,000
Australia
$100,000 – $145,000
Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.
Top 5 Interview Questions — AI / Machine Learning Engineer
1How would you design an ML pipeline for a model that needs to be retrained weekly on new data?
Cover orchestration tools like Airflow or Prefect, data validation, automated retraining triggers, model evaluation gates before promotion, and monitoring for data drift in production.
2What approaches do you use to optimise a model for low-latency inference in production?
Talk about model quantisation, ONNX conversion, batching strategies, TensorRT, and caching for repeated inputs. Show you understand the trade-off between latency, throughput, and accuracy.
3How do you evaluate and select between different LLMs for a specific application?
Mention benchmark-driven evaluation (MMLU, HumanEval for code), domain-specific test sets, cost per token, context window requirements, latency, and fine-tuning flexibility. Show you think beyond just "which model is newest".
4Describe how you have implemented RAG or another retrieval-augmented approach.
Be specific about the chunking strategy, embedding model, vector database used (Pinecone, Weaviate, pgvector), retrieval method, and how you evaluated retrieval quality. Generic answers won't impress here.
5Tell me about a time a model performed well in testing but degraded in production.
Show your understanding of training-serving skew, data drift, and label shift. Explain the monitoring you put in place and how you diagnosed and fixed the issue.
How to Tailor Your CV
OpenAI, Anthropic, and DeepMind want exceptional ML fundamentals, publications or notable open-source contributions, and deep experience with large-scale distributed training. Companies like Palantir and Scale AI want production ML systems experience and the ability to build reliable pipelines on messy real-world data. For startups and growth companies, show your ability to ship fast — a deployed product with real users beats a perfectly engineered system that never launched.