MACHINE LEARNING ENGINEER JOB DESCRIPTION

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A Machine Learning Engineer is responsible for designing, developing, deploying and improving machine learning models and systems.

This can include preparing data, selecting algorithms, training models, evaluating performance, building pipelines, deploying models into production and monitoring how they perform over time.

Machine Learning Engineers, often called ML Engineers, usually work closely with data scientists, AI engineers, data engineers, software engineers, product managers, cloud teams and business stakeholders.



The exact responsibilities can vary depending on the organisation’s maturity and the purpose of the role. Some Machine Learning Engineers focus on research and experimentation. Others work more heavily on production systems, MLOps, model deployment, cloud infrastructure or AI-enabled products.

A Machine Learning Engineer is responsible for designing, developing, deploying and improving machine learning models and systems.

This can include preparing data, selecting algorithms, training models, evaluating performance, building pipelines, deploying models into production and monitoring how they perform over time.

Machine Learning Engineers often work closely with data scientists, AI engineers, data engineers, software engineers, product managers, cloud teams and business stakeholders.

The exact responsibilities can vary depending on the organization’s maturity and the purpose of the role. Some Machine Learning Engineers focus on research and experimentation. Others work more heavily on production systems, MLOps, model deployment, cloud infrastructure or AI-enabled products.

A Machine Learning Engineer may be responsible for:

• designing, building and improving machine learning models
• preparing, cleaning and transforming data for ML use cases
• developing feature engineering processes
• selecting and testing machine learning algorithms
• training, validating and evaluating models
• deploying models into production environments
• building APIs or services that expose ML models to applications
• monitoring model performance, drift and reliability
• improving model accuracy, scalability and maintainability
• supporting MLOps processes and model lifecycle management
• working with data engineers to improve data pipelines
• collaborating with data scientists, AI engineers and software engineers
• documenting model behavior, assumptions and technical decisions
• supporting responsible AI, privacy and governance requirements
• translating business problems into practical machine learning solutions

A strong Machine Learning Engineer usually combines machine learning knowledge, software engineering, data understanding and practical problem-solving.

Common Machine Learning Engineer skills include:

Python

SQL

machine learning algorithms

statistical modelling

feature engineering

model training and evaluation

MLOps

data pipelines

APIs and system integration

cloud platforms

software engineering

Docker and Kubernetes

CI/CD

model monitoring

testing and experimentation

data quality

responsible AI understanding

communication

problem-solving

product thinking

The exact skill set will depend on the role. A research-focused Machine Learning Engineer may need stronger statistics, modelling and experimentation experience. A production-focused Machine Learning Engineer may need stronger MLOps, cloud, APIs, monitoring and software engineering experience.

Machine Learning Engineers may work with a wide range of tools depending on the organisation’s data and AI environment.

Common tools and technologies may include:

Programming and querying: Python, SQL, R, Scala, Java

ML frameworks: PyTorch, TensorFlow, scikit-learn, Keras, XGBoost

Cloud platforms: AWS, Azure, Google Cloud

Data platforms: Databricks, Snowflake, BigQuery, Redshift, Synapse

MLOps and deployment: MLflow, Kubeflow, Docker, Kubernetes, CI/CD

Data processing: Spark, Kafka, Airflow

Experimentation and notebooks: Jupyter, Databricks notebooks

Monitoring: model monitoring, drift detection, logging and observability tools

LLM and GenAI tools: OpenAI, Anthropic, Hugging Face, LangChain, LlamaIndex

Vector databases and search: Pinecone, Weaviate, Chroma, FAISS, Elasticsearch

However, employers should avoid turning the job description into a long list of every tool available. It is usually better to separate essential machine learning and engineering skills from desirable platform or framework experience.

We are looking for a Machine Learning Engineer to design, build and improve machine learning models and systems.

The successful candidate will work with data, engineering, product and business teams to develop practical machine learning solutions that support real business needs.

You will be responsible for preparing data, training and evaluating models, supporting model deployment, improving performance and helping machine learning systems operate reliably in production.

Key Responsibilities

Design, build and improve machine learning models and systems.

Prepare, clean and transform data for machine learning use cases.

Develop feature engineering processes.

Train, validate and evaluate machine learning models.

Deploy models into production environments.

Build APIs or services that connect models to applications.

Monitor model performance, drift and reliability.

Support MLOps processes and model lifecycle management.

Work with data engineers to improve data pipelines and data quality.

Collaborate with data scientists, AI engineers, software engineers and product teams.

Document model behaviour, assumptions and technical decisions.

Support responsible AI, privacy, security and governance requirements.

Required Skills and Experience

Experience in machine learning engineering, data science, software engineering or applied AI.

Strong Python skills.

Strong SQL or data querying experience.

Experience building, training or evaluating machine learning models.

Experience with ML frameworks such as PyTorch, TensorFlow or scikit-learn.

Understanding of feature engineering, model validation and performance evaluation.

Experience working with cloud platforms such as AWS, Azure or Google Cloud.

Experience with data pipelines, databases or data platforms.

Strong software engineering and problem-solving skills.

Strong communication and collaboration skills.

Desirable Skills

Experience with MLOps tools such as MLflow, Kubeflow or similar platforms.

Experience with Docker, Kubernetes, CI/CD or infrastructure tooling.

Experience with Databricks, Snowflake, BigQuery or similar data platforms.

Experience with Spark, Kafka, Airflow or large-scale data processing.

Knowledge of model monitoring, drift detection and observability.

Experience with LLMs, GenAI, RAG or vector databases.

Understanding of responsible AI, explainability, privacy and governance.

Experience taking machine learning models from prototype to production.

Machine Learning Engineer salaries and day rates can vary significantly depending on location, seniority, technical depth, industry, working model and whether the role is permanent or contract.

Employers should benchmark salary before going to market, especially for roles requiring production machine learning, MLOps, cloud platforms, LLMs, GenAI, model monitoring or senior technical leadership.

Candidates will often consider the full package, not just base salary. Remote flexibility, bonus, equity, pension, learning opportunities, technical ownership, access to quality data, product impact and career progression can all influence whether a role is attractive.



KDR can support employers with salary benchmarking and market insight before a search begins.

Hiring a Machine Learning Engineer starts with understanding what kind of machine learning problem the organisation needs to solve.

Before going to market, employers should be clear on:

whether the role is focused on research, model development, MLOps, production systems, AI products or platform engineering

what machine learning models, products or workflows the person will build or improve

whether the work is experimental, production-focused or a blend of both

which tools and platforms are essential

what data environment the person will work with

what level of software engineering experience is required

what level of ownership the person will have

what salary or day rate range is realistic

how the hiring process will assess practical machine learning ability

KDR helps organisations hire Machine Learning Engineers across permanent, contract and senior roles. We can support role definition, candidate search, shortlist creation, interview coordination, salary advice and offer management.

Machine Learning Engineers can move in several directions as their experience grows.

Some progress into Senior Machine Learning Engineer, Lead Machine Learning Engineer, Principal Machine Learning Engineer, Machine Learning Architect or Head of Machine Learning roles. Others move towards AI engineering, MLOps, AI platform engineering, data science, software architecture or AI product leadership.

A typical career path may include:

Data Scientist, Software Engineer, Data Engineer or Research Scientist

Machine Learning Engineer

Senior Machine Learning Engineer

Lead Machine Learning Engineer

Principal Machine Learning Engineer

Machine Learning Architect

Head of Machine Learning

Director of AI or Machine Learning

Chief AI Officer



Career progression often depends on technical depth, production ML experience, software engineering quality, stakeholder influence, architecture ownership and the ability to connect machine learning delivery to business outcomes.