Overview: We are looking for an experience machine learning engineer at Khonology who will part of a dynamic team responsible for developing cutting-edge AI and ML solutions.
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As part of the engineering team, you will play a critical role in ensuring the seamless integration, deployment, and maintenance of ML models in scalable, efficient, and reliable software applications hosted on AWS and on prem.
Objectives Develop and deploy machine learning models.
Enhance existing Al and ML frameworks.
Implement Al-driven solutions for business challenges.
Ensure model accuracy, scalability, and performance.
Facilitate continuous improvement through model monitoring and maintenance.
Responsibilities: Collaborate with the Data Science team to understand ML models and translate them into production-ready applications.
Build and maintain scalable, secure, and high-performing software applications for hosting ML models.
Design, implement, and manage CI/CD pipelines to streamline deployment and integration processes.
Develop and optimize deployment architectures on AWS, leveraging services such as Amazon SageMaker, Lambda, ECS/EKS, and S3.
Ensure robust monitoring, logging, and alerting mechanisms for deployed solutions.
Automate operational workflows, including model retraining, validation, and deployment processes.
Maintain high software quality through code reviews, rigorous testing, and adherence to best practices.
Collaborate with DevOps teams to enhance infrastructure reliability and performance.
Explore and integrate solutions utilizing Large Language Models (LLMs) where applicable.
Stay up-to-date with advancements in ML technologies and AWS services to drive continuous improvement.
Requirements: Experience : 5+ years in software engineering, with a focus on deploying ML models in production.
Strong experience with AWS services, including but not limited to SageMaker, Lambda, ECS/EKS, S3, and CloudWatch.
Hands-on experience in setting up and managing CI/CD pipelines using tools like Jenkins, GitHub Actions, or AWS CodePipeline.
Proven expertise in containerization and orchestration (Docker, Kubernetes).
Strong programming skills in languages such as Python, Java, NodeJs Experience working in a DevOps-driven environment with automation at its core.
Experience with data preprocessing, feature engineering, and model tuning.
Preferred Qualifications and Skills: Bachelor's degree in Computer Science, Software Engineering, or a related field.
Equivalent work experience will also be considered.
Experience with big data technologies such as Hadoop, Spark, etc.
Knowledge of deep learning techniques and frameworks.
Previous experience in deploying AI/ML models in production environments.
Contributions to open-source AI/ML projects or research publications.
AWS Certified Machine Learning (beneficial) AWS Certified Solutions Architect or AWS Certified Developer (beneficial)