Overview
Hybrid
$60 - $65
Contract - W2
Contract - Independent
Contract - 12 Month(s)
50% Travel
Skills
Algorithms
Amazon Web Services
Apache Spark
Cloud Computing
Continuous Delivery
Deep Learning
Data Security
Data Science
Kubernetes
Keras
Machine Learning (ML)
Python
SQL
Scalability
Job Details
Job Summary:
We are looking for a skilled ML/AI/ML Ops Engineer to design, develop, deploy, and manage scalable machine learning and artificial intelligence solutions. The ideal candidate will have a strong background in machine learning, model lifecycle management, cloud platforms, and MLOps practices to operationalize AI/ML pipelines in production.
Key Responsibilities:
- Design, build, and deploy scalable ML/AI models into production environments.
- Develop and automate end-to-end ML pipelines (data ingestion, preprocessing, training, validation, deployment, and monitoring).
- Implement MLOps best practices including CI/CD for ML, model versioning, automated retraining, and monitoring.
- Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions.
- Optimize ML models for performance, scalability, and cost-efficiency on cloud and on-prem platforms.
- Ensure data security, compliance, and governance in ML workflows.
- Integrate ML/AI solutions with APIs, microservices, and business applications.
- Research and evaluate new AI/ML technologies, tools, and frameworks to enhance solution capabilities.
Required Skills & Qualifications:
- Bachelor s or Master s in Computer Science, Data Science, AI/ML, or related field.
- Strong experience with machine learning algorithms, deep learning, and AI frameworks (TensorFlow, PyTorch, Scikit-learn, Keras, Hugging Face).
- Hands-on with MLOps tools and platforms (Kubeflow, MLflow, SageMaker, Vertex AI, Azure ML).
- Proficiency in Python, SQL, and data engineering tools (Spark, Airflow, Kafka).
- Experience with CI/CD pipelines, containerization (Docker, Kubernetes), and cloud services (AWS, Google Cloud Platform, Azure).
- Knowledge of model monitoring, logging, and drift detection.
- Familiarity with NLP, computer vision, or generative AI is a plus.
Preferred Qualifications:
- Certifications in AI/ML, Cloud, or MLOps.
- Experience with large language models (LLMs) and Generative AI.
- Exposure to DevOps/SRE practices in ML environments.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.