Software Engineer

Do you have experience in Software engineering?, Do you have a Bachelor’s degree?, * Master’s degree or higher in Computer Science, Data Science, Mathematics, Engineering, or a related field, or equivalent practical experience is highly preferred. A Bachelor’s degree is required as a minimum.

Technical Experience (3+ Years Required):

• 3+ years of professional software development experience with strong proficiency in Python; familiarity with R is a plus.
• Proven hands-on industry experience deploying, scaling, monitoring, and maintaining production ML models on AWS infrastructure.
• Strong experience building scalable, end-to-end AI/ML data pipelines, including data ingestion, preprocessing, automated feature engineering, and feature selection for real-world AI systems.
• Experience deploying machine learning and deep learning models into production using frameworks such as PyTorch, TensorFlow/Keras, Scikit-learn, or XGBoost.
• Experience with MLOps workflows, CI/CD pipelines, model lifecycle management, and automated deployment processes.
• Strong AWS experience with services such as SageMaker, Lambda, ECS/ECR, EC2, API Gateway, S3, CloudWatch, and SQS, as well as Docker containerization.
• Experience building scalable backend APIs and real-time ML inference services.
• Experience with monitoring, logging, debugging, and optimizing production ML systems.
• Experience working with relational databases and writing efficient SQL queries.
• Strong knowledge of data structures, algorithms, and software engineering best practices.
• Experience with exploratory data analysis (EDA), model interpretation, data visualization, and production analytics.
• Knowledge of optimization techniques using classical methods as well as AI and machine learning algorithms within software development workflows.
• Experience supporting production systems serving real customers or operational environments.

Soft Skills and Team Collaboration:

• Ability to work effectively in a small, fast-moving, cross-functional startup environment.
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders.
• Willingness to collaborate closely with product managers, backend engineers, and operations teams.
• Comfortable taking ownership of systems and driving projects from concept through production deployment and ongoing maintenance.
• Proactive in sharing knowledge, giving and receiving feedback, and contributing to team learning.
• Adaptable and eager to learn in a rapidly evolving startup environment.
• Ability to interact professionally with customers as needed.

Preferred Qualifications:

• Experience with Kubernetes/EKS and infrastructure-as-code tools such as Terraform.
• Experience with streaming or real-time data systems such as Kafka or AWS Kinesis.
• Experience with telematics, transportation, energy systems, electric vehicles (EV), or IoT data.
• Experience with time-series forecasting and predictive analytics.
• Familiarity with ML monitoring and observability tools.
• Familiarity with web technologies such as HTML, JavaScript, and related frameworks.
• Experience developing mobile applications for iOS and Android platforms., * Bachelor’s (Required), * AL/ML Programming: 2 years (Required)

$82,000 - $120,000 a year - Full-time, Contract, Pulled from the full job description

• 401(k)
• Health insurance
• Vision insurance
• Dental insurance, * 401(k)
• Dental insurance
• Health insurance
• Vision insurance

MOEV Inc. (https://www.moev.ai/) is a California-based startup with operations in Los Angeles and Silicon Valley, specializing in Artificial Intelligence (AI) and Machine Learning (ML) technologies for smart fleet management, including electric vehicle charging management, yard management, and dispatch operations., MOEV’s Los Angeles office is seeking a full-time, in-office Machine Learning Engineer (AWS/MLOps) to design, deploy, monitor, and scale production-grade machine learning systems in a fast-growing startup environment. This role is focused on deploying real-world ML systems into production cloud environments and maintaining reliable AI infrastructure that supports operational EV fleet applications.