Junior Machine Learning Engineer

About the Role

The Junior Machine Learning Engineer works within an Applied AI and Machine Learning delivery team to develop, test, deploy, and support AI and machine learning solutions. This early-career role is designed for candidates with strong programming and analytical foundations who want to build scalable, production-ready AI products under the guidance of senior engineers, data scientists, and technical leads.

Key Responsibilities

• Assist in collecting, cleaning, transforming, profiling, and validating structured and unstructured data.
• Perform exploratory data analysis to identify patterns, anomalies, trends, data quality issues, and potential features.
• Develop and evaluate baseline machine learning models under guidance using appropriate algorithms and metrics.
• Support use cases involving classification, regression, clustering, anomaly detection, forecasting, natural language processing, and Generative AI.
• Write reusable, readable, secure, and testable Python code aligned with team coding standards.
• Help convert notebooks and experiments into modular code, repeatable pipelines, APIs, or scheduled workloads.
• Assist with feature engineering, model comparison, hyperparameter tuning, error analysis, and experiment tracking.
• Support deployment of models through APIs, containers, batch jobs, or approved cloud services.
• Help monitor model quality, data drift, model drift, latency, failures, and operational performance.
• Participate in code reviews, pair programming, Agile ceremonies, technical discussions, and knowledge-sharing sessions.
• Document datasets, experiments, assumptions, model results, dependencies, limitations, and implementation decisions.
• Identify and escalate data privacy, security, compliance, responsible AI, and model-performance risks.

Data Preparation and Exploration

• Perform data cleaning, feature preparation, validation, and exploratory analysis.
• Use Python libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn.
• Document data quality findings and support corrective actions.

Model Development

• Build baseline machine learning models and compare performance using suitable evaluation metrics.
• Support feature engineering, error analysis, and hyperparameter tuning.
• Assist with consumption of foundation models and Large Language Models.

Machine Learning Engineering

• Use Git for source control and collaboration.
• Package basic applications or models using Docker.
• Support APIs, pipelines, experiment tracking, and introductory MLOps practices.

Testing and Monitoring

• Write unit tests and data validation checks.
• Assist in testing model behavior and production performance.
• Support logging, monitoring, troubleshooting, and incident documentation.

Requirements & Tech Stack

• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, Engineering, or a related area.
• 0 to 2 years of relevant experience, including internships, academic projects, research, hackathons, or personal projects.
• Working knowledge of Python and SQL.
• Understanding of statistics, probability, linear algebra, supervised learning, and unsupervised learning.
• Ability to communicate technical findings clearly and work effectively in a collaborative Agile environment.
• Strong analytical, problem-solving, documentation, and continuous-learning abilities.

Preferred Skills

• Exposure to Azure Machine Learning, Azure AI services, or Azure AI Foundry.
• Familiarity with PyTorch, TensorFlow, or similar frameworks.
• Exposure to Large Language Models, prompt engineering, embeddings, vector databases, or retrieval-augmented generation.
• Familiarity with Claude Code, Codex, or similar agents.
• Familiarity with Docker, Kubernetes, REST APIs, Linux, GitHub, and MLflow or similar experiment tracking.
• Awareness of responsible AI, privacy, explainability, and AI governance.

Mandatory Pre-Joining Technology Training

Selected candidates must complete the assigned role-specific learning plan, hands-on exercises, knowledge checks, and readiness assessment before joining or deployment.

• Python programming and object-oriented programming
• NumPy, Pandas, data visualization, and SQL
• Statistics and probability for machine learning
• Supervised and unsupervised machine learning
• Model evaluation, feature engineering, and error analysis
• Introduction to deep learning and natural language processing
• Generative AI, Large Language Models, prompt engineering, embeddings, vector databases, and RAG
• Azure Machine Learning, Azure AI services, and Azure AI Foundry
• Git, GitHub, Linux, Docker, REST APIs, and testing fundamentals
• MLOps, experiment tracking, model monitoring, responsible AI, Agile, and Azure DevOps

Pre-Joining Capstone

Build and demonstrate an end-to-end machine learning solution covering data preparation, model development, evaluation, packaging, documentation, and basic deployment or inference.