Experience Level: Overall 10+years (with up to the last 1 year of Gen AI Experience)

Skills – Must Have

Strong Data Analytics

• Advanced programming skills (Python/R)
• MLOps proficiency for model deployment
• Keen understanding of statistical analysis methods

Machine Learning (ML)

• Deep Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• AutoML

An AI/ML Engineer (Local Preferred) is a specialized role focused on the development and deployment of artificial intelligence (AI) and machine learning (ML) models, with a preference for candidates located locally or within a specific geographic region. The job entails designing, implementing, and optimizing AI-driven solutions to solve business problems, using a variety of machine learning techniques and tools. The local preference often reflects a desire for proximity to the company’s offices, allowing for easier collaboration, communication, and sometimes in-person interaction.

Key Responsibilities of an AI/ML Engineer (Local Preferred)

1. Model Development and Optimization:

   • Design, build, and optimize machine learning models to solve specific business problems. This could include tasks like classification, regression, clustering, and time series analysis, depending on the nature of the problem.
   • Experiment with various machine learning algorithms, such as decision trees, random forests, support vector machines (SVM), and deep learning models like neural networks.
   • Optimize models for performance, ensuring they are accurate, efficient, and scalable when deployed in production environments.

2. Data Processing and Feature Engineering:

   • Work with large datasets to clean, preprocess, and transform raw data into meaningful features that can be fed into machine learning models.
   • Perform data wrangling tasks such as handling missing data, normalizing features, and addressing class imbalance in datasets.
   • Conduct exploratory data analysis (EDA) to uncover patterns and relationships in the data that can inform model selection.

3. Collaborating with Cross-Functional Teams:

   • AI/ML Engineers work closely with data scientists, product managers, and software engineers to ensure that the AI models and systems align with business objectives.
   • They often collaborate on defining project goals, translating business needs into technical requirements, and ensuring that AI/ML solutions are scalable, effective, and meet the organization’s technical standards.
   • Depending on the local nature of the role, frequent face-to-face collaboration or in-office meetings may be required.

4. Deployment and Integration:

   • Once the model is trained and tested, AI/ML engineers are responsible for deploying it into production environments where it can be used to make real-time predictions or analyses.
   • This includes integrating machine learning models into existing applications, platforms, or databases.
   • They might use tools such as Docker, Kubernetes, MLflow, or TensorFlow Serving for model deployment and containerization.