Machine Learning Engineer

iConsultera; Remote; GBP 50,000 - 70,000; Full time

Job summary

A leading tech consultancy in the UK is seeking a highly skilled Machine Learning Engineer to design, build, deploy, and optimize machine learning models for data-driven solutions. This role requires strong Python programming skills and experience with ML frameworks, cloud platforms, and collaborative work in a remote environment. The candidate will engage in model deployment, data engineering, and continuous improvement practices within diverse teams, addressing complex business needs through machine learning applications.

Qualifications

• 3+ years of experience in Machine Learning or related roles.
• Strong programming skills in Python.
• Experience deploying ML models into production environments.

Responsibilities

• Design, develop, train, and evaluate machine learning models.
• Deploy ML models into production using scalable architectures.
• Collaborate with data engineers on data ingestion and transformation.

Skills

Python

Machine Learning frameworks (TensorFlow, PyTorch, scikit-learn, XGBoost)

SQL

Data processing tools (Pandas, NumPy, Spark)

APIs (FastAPI, Flask)

Job description

Position Overview

• We are seeking a highly skilled Machine Learning Engineer to design, build, deploy, and optimise machine learning models that power data-driven products and business solutions.
• This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance.
• The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms, and is comfortable working in a fully remote, collaborative environment within the UK.

Key Responsibilities

1. Machine Learning Model Development

• Design, develop, train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases.
• Apply supervised, unsupervised, and deep learning techniques as appropriate.
• Perform feature engineering, model tuning, and validation to improve accuracy and performance.

2. Productionisation & Deployment

• Deploy ML models into production using scalable, reliable architectures.
• Build and maintain APIs or batch pipelines for model inference.
• Monitor model performance, data drift, and retraining needs.

3. Data Engineering & Pipelines

• Collaborate with data engineers to design efficient data ingestion and transformation pipelines.
• Work with structured and unstructured data from databases, APIs, and data lakes.
• Ensure data quality, reproducibility, and versioning.

4. MLOps & Automation

• Implement MLOps practices including CI / CD for ML, model versioning, and experiment tracking.
• Use tools such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Automate model training, testing, deployment, and monitoring workflows.

5. Cloud & Infrastructure

• Build ML solutions on cloud platforms such as AWS, Azure, or GCP.
• Use containerization and orchestration tools (Docker, Kubernetes).
• Optimize compute costs and performance for training and inference workloads.

6. Collaboration & Stakeholder Engagement

• Work closely with Data Scientists, Product Managers, Software Engineers, and Analysts.
• Translate business requirements into scalable ML solutions.
• Communicate model behaviour, limitations, and results clearly to non-technical stakeholders.

7. Research & Continuous Improvement

• Stay current with advancements in machine learning, AI, and data science.
• Evaluate new algorithms, tools, and frameworks for potential adoption.
• Contribute to best practices, documentation, and knowledge sharing.

Required Skills & Experience

Core Technical Skills

• 3+ years of experience in Machine Learning, Data Science, or related roles.
• Strong programming skills in Python.
• Experience with ML frameworks : TensorFlow, PyTorch, scikit-learn, XGBoost.
• Solid understanding of ML algorithms, statistics, and evaluation metrics.
• Experience deploying ML models into production environments.

Data & Engineering Skills

• Strong SQL skills and experience working with large datasets.
• Familiarity with data processing tools (Pandas, NumPy, Spark).
• Experience building APIs (FastAPI, Flask) for ML services.