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Key Responsibilities and Required Skills for a Machine Learning (Train) Engineer

💰 $120,000 - $190,000

Machine LearningData ScienceSoftware EngineeringArtificial Intelligence

🎯 Role Definition

Are you ready to be the "Train Engineer" of our next generation of intelligent products? We are searching for a highly skilled and motivated Machine Learning Engineer to join our innovative technology team. In this pivotal role, you will be the architect and builder of our machine learning systems, responsible for taking AI models from conceptualization to full-scale production. You will work at the intersection of data science, software engineering, and DevOps to design, implement, and maintain scalable ML pipelines. Your work will directly impact our products and business decisions, driving efficiency and creating new capabilities through the power of artificial intelligence. This isn't just about training models; it's about engineering robust, scalable, and impactful AI solutions.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Software Engineer (with a focus on data or backend systems)
  • Data Scientist (with strong programming and production experience)
  • Data Analyst (with advanced technical and modeling skills)

Advancement To:

  • Senior / Staff Machine Learning Engineer
  • MLOps Architect or Specialist
  • AI Research Scientist
  • Engineering Manager (ML/AI Team)

Lateral Moves:

  • Data Architect
  • Senior Data Engineer

Core Responsibilities

Primary Functions

  • Design, develop, and implement scalable, end-to-end machine learning pipelines, encompassing data ingestion, feature engineering, model training, and validation.
  • Translate complex business challenges into precise machine learning problems, selecting the appropriate models, algorithms, and evaluation metrics.
  • Build and maintain robust data processing systems to clean, transform, and version large, complex datasets for model training and evaluation.
  • Deploy machine learning models into production environments using modern MLOps practices, including containerization (Docker, Kubernetes) and CI/CD for ML.
  • Develop and expose model inference services using scalable APIs (e.g., REST, gRPC) and ensure they meet latency and throughput requirements.
  • Monitor the performance, accuracy, and health of production models, implementing systems to detect model drift and data degradation over time.
  • Lead the full lifecycle of retraining and updating models in production to ensure they remain effective and adapt to new data patterns.
  • Collaborate closely with data scientists to productionize their research and prototypes, optimizing algorithms for performance, scalability, and reliability.
  • Write high-quality, maintainable, and well-tested code in Python, adhering to software engineering best practices.
  • Optimize machine learning models and infrastructure for performance and cost-effectiveness across cloud platforms like AWS, GCP, or Azure.
  • Conduct rigorous A/B testing and other experimentation frameworks to evaluate the impact of new models and features on business outcomes.
  • Engineer sophisticated feature stores and feature engineering pipelines to provide high-quality, consistent inputs for multiple ML models.
  • Implement and manage MLOps tooling for experiment tracking, model registry, and workflow orchestration (e.g., MLflow, Kubeflow, Airflow).
  • Stay at the forefront of machine learning, deep learning, and MLOps research, and champion the adoption of new technologies and methodologies.
  • Ensure the ethical and responsible implementation of AI, addressing potential biases and ensuring model fairness and interpretability.
  • Create and maintain comprehensive documentation for ML systems, models, and processes to support team collaboration and knowledge sharing.
  • Partner with product managers and business stakeholders to understand requirements and communicate the capabilities and limitations of ML solutions.
  • Troubleshoot and resolve complex issues in production ML systems, from data pipeline failures to model inference errors.
  • Perform deep-dive analysis on model behavior and system performance to identify opportunities for continuous improvement.
  • Mentor junior engineers and data scientists, providing technical guidance and fostering a culture of engineering excellence within the team.
  • Architect and build infrastructure for distributed training of large-scale models on massive datasets using frameworks like Spark or Ray.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.

Required Skills & Competencies

Hard Skills (Technical)

  • Programming: Expert-level proficiency in Python and its data science ecosystem (Pandas, NumPy, Scikit-learn).
  • ML/DL Frameworks: Hands-on experience with at least one major deep learning framework such as TensorFlow, PyTorch, or Keras.
  • Data & Big Data: Strong SQL skills and experience with big data technologies like Apache Spark, Hadoop, or Dask.
  • Cloud Platforms: Proven experience working with a major cloud provider (AWS, GCP, Azure) and their AI/ML services (e.g., SageMaker, Vertex AI, Azure ML).
  • MLOps: Deep understanding and practical application of MLOps principles, including CI/CD, containerization (Docker), and orchestration (Kubernetes, Kubeflow, Airflow).
  • Model Deployment: Experience serving models in a production environment via REST APIs (using frameworks like FastAPI or Flask).
  • Software Engineering: Strong foundation in software engineering principles, including data structures, algorithms, version control (Git), and testing.
  • Databases: Familiarity with both relational (e.g., PostgreSQL) and NoSQL (e.g., DynamoDB, MongoDB) databases.
  • Distributed Computing: Knowledge of distributed systems and how to build scalable solutions for data processing and model training.
  • Monitoring & Observability: Experience with tools for monitoring application and model performance (e.g., Prometheus, Grafana, Datadog).

Soft Skills

  • Problem-Solving: An analytical and creative mindset to break down complex problems and devise effective, scalable solutions.
  • Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
  • Collaboration: A strong team player who thrives in a collaborative environment, working effectively with data scientists, engineers, and product managers.
  • Ownership & Accountability: A proactive, self-starting attitude with a strong sense of ownership and a commitment to delivering high-quality results.
  • Business Acumen: The ability to connect technical work to business impact and prioritize tasks based on value.
  • Adaptability & Curiosity: A continuous learner who stays current with the fast-evolving AI/ML landscape and is eager to adapt to new tools and techniques.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in a quantitative or technical field.

Preferred Education:

  • Master's degree or Ph.D. in a relevant field.

Relevant Fields of Study:

  • Computer Science
  • Data Science
  • Statistics
  • Mathematics
  • Electrical or Computer Engineering

Experience Requirements

Typical Experience Range: 3-5+ years of hands-on experience in a machine learning engineering, data science, or related software engineering role.

Preferred:

  • 5+ years of experience with a proven track record of deploying and managing machine learning models in a large-scale production environment.
  • Demonstrable experience building ML platforms or infrastructure from the ground up.
  • Contributions to open-source ML projects or publications in relevant conferences.
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