Job Description : AI / ML Engineer
We are seeking a highly skilled and experienced AI / ML Engineer to design, build, and deploy cutting-edge machine learning solutions that drive business value.
The ideal candidate will have a strong foundation in MLOps, deep learning, and scalable cloud deployment.
Experience :
- 5+ years of hands-on experience in AI / ML development, building and delivering production-grade machine learning models.
- Proven expertise in Python programming and extensive experience with core ML frameworks.
- Cloud deployment experience (AWS, Azure, or GCP) is highly preferred.
Key Responsibilities :
The AI / ML Engineer will be responsible for the full lifecycle of machine learning solutions :
1. Model Development and Training :
Build, train, and validate high-performance machine learning and deep learning models to solve complex business problems.Perform extensive data preprocessing, cleaning, and feature engineering to prepare large, complex datasets for modeling.Benchmark models and optimize performance metrics relevant to the business objective.2. MLOps and Deployment :
Containerize models using Docker and prepare them for deployment in cloud environments.Manage the deployment of models onto cloud platforms (AWS, Azure, or GCP) using orchestration tools like Kubernetes and serving solutions via REST APIs.Utilize MLOps tools like MLflow for experiment tracking, model registry, and reproducible workflows.3. Monitoring, Maintenance, and Collaboration :
Monitor and maintain model performance in production, detecting and addressing model drift, data drift, and performance degradation.Design and implement feedback loops to continuously retrain and improve models.Collaborate with cross-functional teams (Data Scientists, Software Engineers, Product Managers) to understand requirements, define project scope, and integrate ML solutions into production systems.Nice-to-Have Skills :
Experience with data visualization tools and libraries (Matplotlib, Tableau).Familiarity with CI / CD pipelines and strong adoption of software engineering best practices (testing, modular design).Exceptional communication and documentation skills.Awareness of Responsible AI principles and data ethics in machine learning development.(ref : hirist.tech)