Description:

The Machine Learning Engineer (The AI Innovator) role involves designing, implementing, and optimizing machine learning models to solve real-world problems.
You will work closely with cross-functional teams to develop intelligent algorithms, process large datasets, and deploy machine learning models that enhance product functionality.
Key responsibilities include building, training, and deploying machine learning models using frameworks such as TensorFlow, PyTorch, or Scikit-learn for applications like recommendation systems and predictive analytics.
You will collaborate with data engineers and analysts to collect, clean, and prepare large datasets for training and evaluation, designing data pipelines for automation.
The role requires training machine learning models and performing hyperparameter optimization to improve performance based on business needs.
You will work with software engineers to deploy models into production environments, building APIs and ensuring models are scalable and maintainable.
Continuous monitoring of deployed models is essential, along with retraining or fine-tuning them to maintain accuracy and relevance.
You will partner with data scientists, software engineers, and product managers to identify opportunities for machine learning solutions that drive business objectives.
Staying current with the latest advancements in machine learning and AI technologies is crucial, as you will explore new techniques and tools to enhance the team's capabilities.

Requirements:

Strong knowledge of machine learning algorithms such as regression, classification, clustering, deep learning, and reinforcement learning is required.
Proficiency in programming languages such as Python, R, or Java, along with hands-on experience with data manipulation libraries like Pandas, NumPy, or Spark, is necessary.
Experience in handling large datasets, feature engineering, and data preprocessing is essential, including cleaning, normalizing, and transforming raw data.
Familiarity with deploying machine learning models into production environments using cloud platforms (AWS, GCP, Azure) and containerization tools like Docker or Kubernetes is required.
A strong ability to fine-tune models for performance and optimize algorithms for scalability and speed is necessary.
A Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field is required, with equivalent experience in machine learning engineering also valued.
At least 3+ years of experience in machine learning engineering, focusing on building and deploying models in production environments, is required.
Proven experience working with large datasets, designing machine learning pipelines, and optimizing model performance is necessary.
Experience with cloud-based machine learning services (e.g., AWS SageMaker, Google AI Platform, Azure Machine Learning) is highly desirable.

Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums are provided.
Competitive vacation, sick leave, and 20 paid holidays per year are included in the benefits package.
Flexible work schedules and telecommuting options promote work-life balance.
Opportunities for training, certification reimbursement, and career advancement programs are available for professional development.
Access to wellness programs, including gym memberships, health screenings, and mental health resources, is offered.
Life insurance and short-term/long-term disability coverage are part of the benefits.
Confidential counseling and support services for personal and professional challenges are provided through the Employee Assistance Program (EAP).
Financial assistance for continuing education and professional development is available through tuition reimbursement.
Opportunities to participate in community service and volunteer activities are encouraged.
Employee recognition programs celebrate achievements and milestones within the organization.