Production ML Engineer: Build & Deploy Scalable AI Systems

Initvalue; Fremont (CA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Initvalue in Fremont, CA is seeking a skilled Machine Learning Engineer to join our AI/ML team and build production-ready ML solutions for real-world business problems.

You will design, deploy, and continuously improve models and systems, collaborating with data scientists, software engineers, and product teams to deliver scalable ML pipelines and APIs. You will mentor juniors and stay current with AI advances.

Qualifications

• Strong mathematical background in probability and statistics.
• Experience building production ML models and pipelines.
• Proficiency with ML frameworks such as Keras or PyTorch.

Responsibilities

• Design, develop, train, and deploy machine learning models.
• Build scalable ML pipelines for data preparation, training, evaluation, and inference.
• Implement MLOps practices for reliability and maintainability of ML systems.
• Collaborate with data and software engineering teams on end-to-end ML solutions.
• Mentor junior engineers and promote best engineering practices.
• Stay up to date with advancements in machine learning, deep learning, and AI technologies.

Skills

Analytical skills

Math & stats

ML frameworks (Keras/PyTorch)

Data structures & modeling

Time management

Tools

Keras

PyTorch

Job description

We are looking for a highly skilled Machine Learning Engineer to join our AI/ML team and build production-ready machine learning solutions for real-world business problems. The candidate will work closely with data scientists, software engineers, data engineers, and product teams to develop, deploy, and continuously improve machine learning models and systems.

Key Responsibilities

• Design, develop, train, and deploy machine learning models.
• Build scalable ML pipelines for data preparation, training, evaluation, and inference.
• Perform feature engineering, model selection, and hyperparameter optimization.
• Evaluate and improve model accuracy, performance, and scalability.
• Deploy and maintain machine learning models in production.
• Develop APIs and services to integrate ML models with business applications.
• Implement model monitoring, versioning, and retraining processes.
• Collaborate with data and software engineering teams on end-to-end ML solutions.
• Apply MLOps practices to improve the reliability and maintainability of ML systems.
• Contribute to technical architecture and design decisions.
• Mentor junior engineers and promote best engineering practices.
• Stay up to date with advancements in machine learning, deep learning, and AI technologies.

Skills and qualifications

• Impeccable analytical and problem-solving skills
• Extensive math and computer skills, with a deep understanding of probability, statistics, and algorithms
• In-depth knowledge of machine learning frameworks, like Keras or PyTorch
• Familiarity with data structures, data modeling, and software architecture
• Excellent time management and organizational skills