Machine Learning Engineer

Overview

On Site

$40,000 - $60,000

Full Time

Skills

Amazon Web Services

Artificial Intelligence

Continuous Integration

Amazon SageMaker

Apache Kafka

Good Clinical Practice

Data Science

Computer Science

Continuous Delivery

Cloud Computing

Deep Learning

Machine Learning (ML)

Machine Learning Operations (ML Ops)

Job Details

Key Responsibilities

Model Development

Develop, train, and optimize machine learning models (supervised, unsupervised, deep learning, NLP, etc.).
Conduct feature engineering, experimentation, and model evaluation using appropriate metrics.
Implement best practices for reproducible research and model versioning.

ML System Engineering

Build and maintain ML pipelines for data ingestion, training, validation, and deployment.
Develop APIs or microservices to serve ML models at scale.
Optimize model performance, latency, and resource use in production.

Data Engineering & Management

Collaborate with data teams to ensure high-quality, well-structured datasets.
Build scalable data processing workflows (batch and streaming).
Work with cloud services (AWS/Azure/Google Cloud Platform) for data storage, computation, and model deployment.

MLOps & Deployment

Implement CI/CD pipelines for ML systems.
Monitor model health (drift, performance degradation, anomalies).
Manage automated retraining and continuous model improvement.

Collaboration & Communication

Collaborate with product teams to translate business needs into ML solutions.
Communicate complex technical concepts to non-technical stakeholders.
Document experiments, architecture, and deployment processes.

Required Qualifications

Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or related field.
Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Experience with cloud platforms (AWS, Google Cloud Platform, Azure).
Solid understanding of data structures, algorithms, and software engineering principles.
Hands-on experience deploying ML models to production.
Knowledge of containerization (Docker) and orchestration (Kubernetes).

Preferred Qualifications

Experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker).
Familiarity with big data technologies (Spark, Kafka, Hadoop).
Experience with NLP, LLMs, or recommendation systems.
Knowledge of monitoring & observability tools (Prometheus, Grafana, Datadog).
Contributions to open-source ML projects.

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