AI/ML engineer

What matters most

• End-to-end model delivery

  The role builds, trains, evaluates, and deploys ML models. Prepare to explain how you would take a model from a business problem through evaluation to deployment, including how you would judge whether it delivered measurable impact.

• Data pipelines and feature engineering

  The posting explicitly calls for data pipelines, feature-engineering workflows, SQL, and data processing. Be ready to discuss data quality, reproducibility, and how pipeline choices affect model performance.

• LLM and RAG implementation

  The role includes fine-tuning foundation models and implementing RAG architectures; LLMs, prompt engineering, and vector databases are required knowledge. Prepare to explain when you would use retrieval, fine-tuning, or prompting and how you would evaluate the result.

• Production performance and collaboration

  Monitoring, MLOps practices, experimentation, and benchmarking are responsibilities; scalable deployments and stakeholder collaboration are stated success metrics. Prepare to connect technical decisions with performance evidence and communication across product, engineering, and business teams.

Likely challenges to think through

• Choosing among solution approaches

  A likely challenge, not a stated employer problem, is selecting an approach across the advertised NLP, LLM, computer vision, and predictive analytics work rather than defaulting to the most complex model. Prepare a decision framework based on the problem, available data, evaluation criteria, and deployment constraints.

• Balancing quality with scalability

  The posting values both high-quality model performance and scalable deployment. A plausible trade-off is that a stronger model or more elaborate RAG pipeline may cost more to run or take longer to respond. Prepare to describe how you would benchmark alternatives and justify a practical choice.

• Maintaining models after release

  Because the role includes monitoring and continuous improvement, a likely difficulty is deciding when changing production performance warrants investigation or retraining. Prepare to explain what you would monitor, how you would compare results with a baseline, and how you would validate changes before release.

Topics to prepare

• ML evaluation and benchmarking

  Revise train/validation/test separation, leakage, metric selection, and baseline comparisons. Exercise: for a hypothetical predictive-analytics problem, choose a baseline, evaluation metric, and experiment plan. The posting explicitly calls for model evaluation, experimentation, benchmarking, and measurable impact.

• Deep learning and foundation-model fine-tuning

  Revise training objectives, overfitting, transfer learning, and how fine-tuning differs from using a foundation model without updating its weights. Exercise: explain when fine-tuning might be preferable for a hypothetical NLP task and how you would evaluate the result. The role explicitly includes deep learning and foundation-model fine-tuning.

• LLM retrieval and generation

  Revise how prompts, vector databases, retrieval, and generated answers fit together in a RAG architecture. Exercise: diagram a hypothetical document-question-answering flow and identify where retrieval or answer quality could fail. LLMs, prompt engineering, vector databases, and RAG are explicit requirements.

• Data and feature-engineering design

  Revise SQL joins, data quality checks, feature construction, and leakage prevention. Exercise: sketch how raw records would become training features, noting validation checks. The posting calls for SQL, data processing, pipelines, and feature-engineering workflows.

• Deployment and model monitoring

  Revise the concepts of versioning, CI/CD, deployment, performance monitoring, and detecting degradation. Exercise: outline a model's path from evaluated code to a monitored deployment. Cloud platforms, Git, CI/CD, and MLOps concepts are required; deployment and monitoring knowledge is preferred.

Skills to revise

• Build a Python baseline

  Using hypothetical tabular data, practise cleaning it with Pandas and NumPy, training a Scikit-learn baseline, and reporting an appropriate held-out metric. This exercises the named Python libraries and the responsibility to build and evaluate models.

• Write SQL for training data

  Practise joining two hypothetical tables, filtering invalid records, and aggregating features while checking that no future information enters training data. This directly supports the stated SQL, data-processing, and feature-engineering work.

• Train and inspect a neural model

  In PyTorch or TensorFlow, practise a small training and validation loop; record loss, compare predictions, and explain an adjustment you would test. Either framework is listed as required, and the role includes training and evaluating ML models.

• Prototype and assess a RAG flow

  As a preparation exercise, assemble a small hypothetical document set, retrieve relevant passages with a vector database, draft a prompt that uses them, and inspect answers for unsupported claims. This reflects the explicit RAG, vector-database, and prompt-engineering requirements; the prototype is a suggestion, not a stated deliverable.

• Rehearse an ML delivery workflow

  Practise committing a small model change in Git, specifying a CI/CD check, and describing how you would deploy and monitor it on one of Azure, AWS, or GCP. The posting requires familiarity with those cloud options, Git, CI/CD, and MLOps concepts; it does not mandate a particular service.

• Map the stated requirements to evidence

  The role lists 2–5 years of experience, a relevant degree, Python, SQL, PyTorch or TensorFlow, cloud platforms, and MLOps concepts. Prepare a factual one-page summary of your education and any work or examples you can discuss for each; leave gaps visible rather than filling them with claims.

• Prepare an end-to-end ML project walkthrough

  The role covers data pipelines, feature engineering, model training, evaluation, and deployment. If you have a relevant project, prepare a concise walkthrough of the business problem, data processing, model choice, evaluation results, deployment, and measured impact. Otherwise, use a clearly labeled practice example.

• Rehearse a practical Python and SQL explanation

  Prepare a small, explainable example of data processing with SQL and Python, including where NumPy, Pandas, or Scikit-learn fits. Be ready to explain how you would check data quality and avoid leakage; these checks are preparation suggestions, not stated job requirements.

• Sketch an LLM solution design

  The role calls for prompt engineering, vector databases, RAG, and foundation-model fine-tuning. As a design exercise, draw a RAG workflow and explain how you would evaluate retrieval and answers, then identify when fine-tuning might be worth testing instead.

• Plan an experiment and deployment discussion

  The posting names benchmarking, cloud platforms, Git, CI/CD, monitoring, and scalable deployment. Prepare a comparison table of candidate models and metrics, plus a simple deployment diagram showing versioning, release, and performance monitoring. Treat Docker and Kubernetes as preferred exposure, not required tools.

• Prepare a stakeholder collaboration example

  The role involves product, engineering, and business teams. If available, select an example showing how a problem was defined, trade-offs were communicated, and results informed a decision; distinguish measured outcomes from expectations.

• Write targeted questions for the employer

  Prepare questions about the first business problems to tackle, available data and evaluation measures, the current deployment and monitoring setup, and how the Technology Manager or Technical Lead works with product and business teams.