Software Engineer – AI & Machine Learning

Role Overview

Software Engineer – AI & Machine Learning designs, develops, and maintains modern software applications that leverage machine learning, NLP, clustering, and Generative AI to automate defect analysis and improve operational intelligence.

Responsibilities

• Build and maintain scalable applications using Python with web frameworks such as Django, Flask, or FastAPI
• Develop ML models for automated defect analysis and pattern recognition (including clustering and NLP)
• Create AI-powered diagnostic systems that classify, categorize, and route operational tickets
• Design and optimize SQL databases, queries, and data models for analytics and real-time processing
• Develop and maintain RESTful APIs for AI-powered applications and enterprise integrations
• Improve clustering logic to reduce manual intervention in defect categorization
• Collaborate with Product, QA, Engineering, and Operations to translate requirements into technical solutions
• Evaluate and implement emerging AI/ML and engineering technologies
• Participate in Agile ceremonies (sprint planning, code reviews, retrospectives, technical design)
• Drive improvements in performance, scalability, maintainability, and software quality
• Document technical designs, models, standards, and development best practices

Requirements

• 4–8+ years of software development with Python and modern web frameworks
• Strong SQL skills: database design, query optimization, indexing, and performance tuning
• Hands-on ML frameworks: Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy
• Experience with clustering algorithms: K-Means, DBSCAN, Hierarchical Clustering
• NLP experience: text classification, entity extraction, sentiment analysis, and language models
• Generative AI concepts and AI-assisted development workflows
• Knowledge of feature engineering, data preprocessing, model validation, and model optimization
• Experience designing, developing, and consuming REST APIs
• Data pipeline/ETL and real-time data processing
• Experience with relational and NoSQL databases for analytics workloads
• Familiarity with Git, CI/CD, automated testing, and modern DevOps practices
• Bachelor’s degree in CS/CE/Data Science/ML/AI (or related)

Nice-to-Haves

• Not explicitly stated beyond the listed requirements.