AI/ML Senior Developer || Remote.

Role: AI/ML Senior Developer

Location: Remote (Dallas, TX)

Duration: long months contract

Required Skills:

• Minimum 10 years of overall experience.
• Bachelors in computer sciences or similar. Masters preferred.
• Skills At least 5 years hands-on programming experience working on enterprise products Demonstrated proficiency in multiple programming languages with a strong foundation in a statistical platform such as Python, React JS, Typescript.
• The candidate would play the role of an AI/ML Senior Developer/Team Lead participating in Designing, Developing and validating AI/ML solutions leveraging Python / SQL for US Healthcare Customers.
• Experience in Healthcare domain required.
• 3+ years project Experience in Deep Learning/Machine learning, Artificial Intelligence Experience in building AI models using algorithms of Classification & Clustering techniques
• Excellent verbal and written communication skills Ability to work in and define a fast pace and team focused environment
• Proven record of delivering and completing assigned projects and initiatives Ability to deploy large scale solutions to an enterprise estate
• Strong interpersonal skills Understanding of Revenue Cycle Management processes like Claims filing and adjudication is a plus

Responsibilities:

• Participate in research, design, implementation, and optimization of AI Models
• Build AI agents from scratch and help product managers and stakeholders understand results Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products
• Hands on experience in Python Build data ingest and data transformation platform Identify transfer learning opportunities and new training datasets
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and/or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Defining validation strategies Defining the pre-processing or feature engineering to be done on a given dataset
• Fine tune pre-trained models
• Analyzing the errors of the model and designing strategies to overcome them
• Deploying models to production Create APIs and help business customers put results of your AI models into operations

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