Machine Learning Engineer

Overview

On Site

$40,000 - $60,000

Full Time

Skills

Artificial Intelligence

Computer Science

Continuous Improvement

Continuous Delivery

Machine Learning (ML)

Machine Learning Operations (ML Ops)

Computer Vision

Good Clinical Practice

Decision-making

Continuous Integration

Collaboration

Cloud Computing

Amazon SageMaker

Programming Languages

Job Details

We are seeking a skilled Machine Learning Engineer to design, develop, and deploy scalable machine learning models and intelligent systems that drive data-driven decision-making. The ideal candidate has strong foundations in machine learning, data engineering, software development, and cloud technologies.

Key Responsibilities

Model Development & Deployment

Build, train, and optimize machine learning and deep learning models for real-world applications.
Develop end-to-end ML pipelines including data preprocessing, feature engineering, model training, validation, and deployment.
Deploy models to production using MLOps frameworks (e.g., Docker, Kubernetes, MLflow, TensorFlow Serving, SageMaker).

Data Engineering & Analysis

Work closely with data engineers to ensure data quality, integrity, and availability.
Design scalable data pipelines for ingestion, transformation, and storage.
Perform exploratory data analysis (EDA) to uncover insights and guide feature design.

Software Engineering

Write clean, reusable, and efficient code in Python or other programming languages.
Integrate ML models into production-grade systems and APIs.
Collaborate with backend and product teams to embed ML into applications.

Monitoring & Optimization

Monitor model performance and implement continuous improvement.
Detect and mitigate model drift, bias, and performance degradation.
Optimize model speed and accuracy using advanced techniques.

Required Skills & Qualifications

Bachelor s or Master s in Computer Science, Data Science, Engineering, or related field.
Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).
Solid understanding of ML concepts: regression, classification, clustering, NLP, computer vision, recommendation systems, etc.
Experience with MLOps tools (MLflow, Kubeflow, Airflow, CI/CD pipelines).
Hands-on experience with cloud platforms (AWS, Google Cloud Platform, Azure).
Knowledge of data structures, algorithms, and software engineering best practices.

Preferred Qualifications

Experience with deep learning architectures (CNNs, RNNs, Transformers).
Familiarity with big data technologies: Spark, Hadoop, Databricks.
Understanding of LLMs and modern AI frameworks.
Experience with real-time inference systems.
Publications or contributions to open-source ML projects.

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