Role Summary:

The AI Engineer will be responsible to design, develop, and deploy scalable machine learning and deep learning models. This role involves extensive data preprocessing, feature engineering, and exploratory data analysis on diverse data types including structured, text, image, and time-series data. The position also focuses on integrating AI models into production systems through REST APIs or microservices, ensuring robustness and efficiency. Staying current with AI advancements, conducting research, and prototyping innovative algorithms are key aspects of this role. Collaboration with cross-functional teams to translate business challenges into impactful AI solutions is essential.

Key Responsibilities:

Model Development & Deployment:

• Design, develop, train, and optimize machine learning (ML) and deep learning (DL) models.
• Evaluate model performance using appropriate metrics and techniques.
• Deploy ML/DL models to production environments ensuring scalability, robustness, and efficiency.

Data Preprocessing & Analysis:

• Handle data cleaning, transformation, feature engineering, and exploration data analysis (EDA).
• Work with structured and unstructured data, including text, images, and time-series data.

System Integration:

• Integrate trained AI models into existing software applications and production systems.
• Build REST APIs or microservices for model serving and inference.

Research & Innovation:

• Stay up to date with the latest advancements in AI/ML/DL.
• Research and prototype new algorithms and techniques for potential integration.

Collaboration & Communication:

• Work closely with cross-functional teams including software developers, data engineers, product managers, and business stakeholders.
• Translate business problems into AI solutions with measurable impact.

Job Specification:

Basic Qualification & Experience:

• Bachelor’s/Master’s in Computer Science, Software Engineering, or related field.
• Preferably 5 years of professional experience as an AI Engineer.
• Proficiency in Python and strong knowledge of relevant libraries (NumPy, Pandas, etc.).
• Hands-on experience with ML/DL frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Solid understanding of machine learning algorithms, data structures, and optimization techniques.
• Experience with at least one cloud platform (AWS, Google Cloud Platform, Microsoft Azure).

Soft Skills:

• MLOps experience (MLflow, Docker, CI/CD)
• Prior experience in deploying AI models at scale
• Knowledge of big data tools (Spark, Kafka)
• Tech savvy and continuously learning
• Effective collaborator across teams
• Make informed, data-driven decisions
• Manage complex AI/ML challenges
• Results-oriented with business focus
• Clear communicator of technical concepts
• Adaptable to new technologies and research
• Customer-focused and solution-driven