AI Engineer - Software

Responsibilities

• Machine Learning & Model Development
• Design, train, validate, and deploy machine learning models using modern frameworks and best practices.
• Build end-to-end ML pipelines that include data ingestion, preprocessing, feature engineering, training, evaluation, and monitoring.
• Optimize models for performance, scalability, and efficiency.
• Data Wrangling & Analysis
• Collect, clean, transform, and structure complex datasets from diverse sources.
• Perform exploratory data analysis to identify trends, anomalies, and opportunities.
• Develop automation for data processing workflows and ensure high data quality.
• AI Agents & Automation
• Create intelligent agents capable of autonomous decision-making, workflow automation, and contextual reasoning.
• Integrate agents with internal systems, APIs, and knowledge bases.
• Evaluate agent performance and iterate based on measurable outcomes.
• Research & Emerging Technology Exploration
• Stay current with the rapidly evolving AI/ML landscape, including new algorithms, architectures, tools, and best practices.
• Prototype innovative AI solutions using cutting-edge techniques such as LLMs, RAG pipelines, multi-agent systems, and generative models.
• Develop technical briefs, proofs of concept, and recommendations for adopting new technologies.

Qualifications

• Bachelor's of Science degree or Master’s degree in Computer Science, Data Science, or AI Engineering
• 5+ years of post-graduate related experience in developing software applications
• Strong proficiency in Python and familiarity with ML libraries such as TensorFlow, PyTorch, scikit-learn, or similar.
• Strong proficiency with application APIs, web services, and data management approaches in applications
• Experience with data wrangling tools and technologies (Pandas, SQL, ETL systems).
• Solid understanding of machine learning algorithms, statistical modeling, and model evaluation.
• Experience working with LLMs or generative AI models.
• Knowledge of cloud platforms (Azure, AWS, GCP) and containerization technologies.
• Experience building AI agents or autonomous systems.
• Familiarity with vector databases, RAG architectures, or multi-modal models.
• Exposure to MLOps practices (CI/CD, model monitoring, feature stores).
• Contributions to AI research, open-source tools, or AI-related publications.
• Knowledge of distributed computing or GPU optimization.