Machine Learning/Artificial Intelligence Engineer

To be successful as a Machine Learning/Artificial Intelligence Engineer you should have experience with:

• Good years of experience in IT with background in Development, Machine Learning and/or Data analysis.

• Should have hands-on experience of NLP/AI/ML tool & technologies – GPT, BERT or other language models.

• Should have experience in building GenAI applications, RAG based architectures.

• Experience in Model Development: Research, design, implement, and optimize machine learning models for specific use cases such as predictive analytics, NLP, computer vision, and recommender systems.

• Data Preparation: Collect, preprocess, and analyze large datasets to extract meaningful insights and ensure data quality for model training.

• Deployment: Build and deploy Al/ML solutions into production environments using appropriate tools and frameworks.

• Knowledge of one of the cloud platforms is must : AWS/AZURE.

• Collaboration: Work closely with product managers, data engineers, and software developers to integrate Al capabilities into products and ensure alignment with business objectives.

• Performance Monitoring: Evaluate and monitor model performance and accuracy post-deployment, iterating to address challenges and refine models as needed.

• Strong Affinity to stay informed on the latest trends, tools and research in Al and machine learning space .

• Support and contribute to data collection efforts, as needed.

• Verify data quality to ensure accurate analysis and reporting.

• Help identify the business data needed to produce the most useful insights and future analytics.

• Utilize data to make actionable recommendations at all levels.

• Monitor data management processes to ensure data quality and consistency.

• Monitor system performance, data integrity and usage metrics.

• Contribute to data dictionary, standards, training, and ongoing updates.

Some other highly valued skills may include:

• Web service development experience using REST services/APIs, JSON, XML, IVRs, Jenkins, other Cloud Platforms.

• Experience in setting up DevOps pipelines.