Develop and maintain AI/ML models and applications:
- Design and implement machine learning models tailored to specific business needs.
- Continuously monitor and update models to ensure they remain accurate and effective.
- Develop applications that integrate AI/ML models, providing end-to-end solutions for various use cases.
Perform data preprocessing, feature engineering, and model training:
- Clean and preprocess raw data to remove noise and inconsistencies, ensuring high-quality input for models.
- Apply feature engineering techniques to create meaningful features that enhance model performance.
- Train models using appropriate algorithms and techniques, optimizing for accuracy and efficiency.
Collaborate with cross-functional teams to gather requirements and deliver solutions:
- Work closely with stakeholders from different departments to understand their needs and objectives.
- Translate business requirements into technical specifications for AI/ML solutions.
- Deliver tailored solutions that address specific challenges and add value to the organization.
Debug and troubleshoot AI/ML models and applications:
- Identify and resolve issues related to model performance, such as overfitting or underfitting.
- Debug application code to ensure smooth integration and functionality of AI/ML models.
- Perform root cause analysis to address any anomalies or errors in the models and applications.
Participate in code reviews and contribute to team success:
- Engage in regular code reviews to ensure code quality and adherence to best practices.
- Provide constructive feedback to peers and incorporate suggestions to improve your own code.
- Collaborate with team members to achieve project milestones and contribute to the overall success of the team.
Ensure compliance with coding standards and best practices:
- Write clean, maintainable, and well-documented code that follows industry standards.
- Stay updated with the latest coding practices and integrate them into your work.
- Conduct peer reviews to maintain high standards of code quality and consistency.
Continuously improve technical skills and knowledge:
- Engage in continuous learning through online courses, workshops, and reading relevant literature.
- Experiment with new tools and technologies to understand their potential applications. Attend conferences, webinars, and meetups to network with professionals and learn from experts.
Responsibilities
Job Complexity & Problem Solving
- Continuously develop technical skills to keep up with technology advancements.
- Ability to debug and troubleshoot complex AI/ML models.
Leadership & Stakeholder Management
- Ability to develop and maintain high-quality AI/ML models.
- Strong problem-solving and debugging skills.
- Ability to work collaboratively in a team environment.
Technical Competencies
- Working knowledge for cloud development (Containerization, Cloud Services (AWS, Azure), API Design, Microservice Architecture)
- Ability to deploy AI models into production effectively and at scale.
- Strong understanding in LLM techniques, for ex: tokenization, RAG, Reinforcement Learning and etc.
- Experience with data preprocessing, feature engineering, and model training.
- Capability to stay abreast of new research and integrate the latest advancements.
- Academic Qualifications: At least a Bachelor’s degree in Computer Science, Data Science, or a related field.
- Minimum of 2 years of experience in AI/ML development.
- Proficiency in programming languages such as Python/Java.
- Strong problem-solving and debugging skills.
- Ability to work collaboratively in a team environment.
Skills Required
- Bachelor's degree in Computer Science, Data Science, or a related field
- At least 2 years of experience in AI/ML development
- Proficiency in Python or Java
- Strong problem-solving and debugging skills
- Ability to work collaboratively in a team environment
- Working knowledge of cloud development, including containerization and AWS or Azure cloud services
- Knowledge of API design and microservice architecture
- Ability to deploy AI models into production effectively and at scale
- Understanding of LLM techniques, including tokenization, RAG, and reinforcement learning
- Experience with data preprocessing, feature engineering, and model training
What We Do
KGP Services is a leading network services provider and trusted partner to customers who build, own, and operate high-speed fiber, wireless, and cloud networks across North America. We combine complete end-to-end capabilities with a customer-first culture to provide custom services including design, engineering, installation, integration, and maintenance for all technologies. Through our new partnership with Circet, Europe’s largest network services provider, KGP Services is positioned for greater scale and expansion to help customers meet the fast-growing demand for high-speed connectivity.
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