Machine Learning Engineer

iConsultera; Remote; GBP 70,000 - 90,000; Full time

Job summary

A leading tech consultancy is seeking a highly skilled Machine Learning Engineer to design, build, and optimize ML models driving data-driven solutions. The ideal candidate will have over three years of experience in Machine Learning, strong Python experience, and be proficient with ML frameworks like TensorFlow and PyTorch. This role focuses on production-ready ML systems, data engineering, and collaboration with cross-functional teams in a fully remote setup within the UK.

Qualifications

• 3+ years of experience in Machine Learning or Data Science.
• Strong programming skills in Python with ML frameworks.
• Experience deploying ML models in production environments.

Responsibilities

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

Skills

Machine Learning

Python

Data Engineering

SQL

TensorFlow

PyTorch

Docker

Kubernetes

Tools

MLflow

Azure ML

SageMaker

Vertex AI

Spark

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.