Design, build, and refine machine learning (ML) and artificial intelligence (AI) models.
Implement MLOps practices to automate the deployment, monitoring, and maintenance of ML models.
Work closely with cross-functional teams to integrate AIML models into larger systems and applications.
Preprocess, transform, and analyze large datasets for use in AIML model development.
Implement data security measures such as encryption, decryption, and PII data masking.
Use AWS services to facilitate the development and deployment of AIML models.
Maintain and optimize code deployment models including Blue Green Deployment, Canary Deployment, etc.
Use programming languages such as Python, R, and tools like Pandas for data engineering tasks.
Ensure the integrity and reliability of data sources and outputs.
Effectively communicate complex ideas, results, and concepts to stakeholders, both technical and non-technical.
Requirements
Minimum of 5 years of experience in deploying and automating AIML models
Experience with various machine learning algorithms, principles, and frameworks, including supervised, unsupervised, reinforcement learning models, and deep learning techniques.
Minimum of 3 years of data engineering, data science, or data analysis-related work experience.
Strong programming skills, particularly in Python.
Proficient knowledge of data preprocessing, data exploration, and feature engineering.
Experience with SQL, NoSQL databases, and data pipeline tools like Apache Kafka or Apache Beam.
Proficient understanding of software development principles, DevOps methodologies, and cloud computing.
Experience with infrastructure as code (IAC) tools such as Terraform or CloudFormation.
Familiarity with monitoring tools like Prometheus, Grafana, or ELK stack (Elasticsearch, Logstash, Kibana).
Understanding of data privacy, security, and governance practices.
Excellent communication and collaboration skills.
AWS specialty certification in Machine Learning or Data Analytics is preferred.