AI/ML Developer

Role Overview

We are looking for a hands-on AI/ML Engineer to join our growing technology team. In this role, you will be responsible for designing, developing, and deploying machine learning models and AI-powered solutions that solve real business problems. You will work closely with Data Engineers, BI Developers, and business stakeholders to move ideas from experimentation to production.

This role is ideal for someone who is equally comfortable with model building and productionizing - someone who doesn t just train models but ensures they work reliably in the real world.

Key Responsibilities

• Design, develop, and deploy machine learning models across supervised, unsupervised, and deep learning paradigms
• Translate business problems into well-scoped ML problem statements and solution approaches
• Perform end-to-end ML workflow - data preprocessing, feature engineering, model training, evaluation, and deployment
• Build and maintain ML pipelines for both batch inference and real-time prediction
• Collaborate with Data Engineers to ensure feature-ready, high-quality datasets
• Integrate AI/ML models into products and workflows via APIs or embedded solutions
• Monitor deployed models for performance degradation, data drift, and retraining triggers
• Research and evaluate emerging AI/ML techniques, frameworks, and tools relevant to business use cases
• Maintain clear documentation of experiments, model decisions, and deployment architecture
• Contribute to building internal AI capabilities - reusable components, libraries, and best practices

Required Skills Qualifications

• 2+ years of hands-on experience in machine learning, AI development, or a closely related role
• Strong proficiency in Python - including NumPy, Pandas, Scikit-learn, and data processing libraries
• Solid experience with ML frameworks - TensorFlow, PyTorch, or both
• Sound understanding of core ML concepts - regression, classification, clustering, time series, NLP, and model evaluation metrics
• Experience with feature engineering, hyperparameter tuning, and model optimization
• Familiarity with MLOps practices - experiment tracking, model versioning, and pipeline automation (MLflow, Weights Biases, or similar)
• Experience deploying models via REST APIs using FastAPI or Flask
• Working knowledge of cloud platforms - Microsoft Azure (preferred), AWS, or GCP
• Familiarity with version control (Git) and collaborative development workflows
• Bachelors degree in Computer Science, Data Science, Mathematics, or a related field

Good to Have

• Hands-on experience with Large Language Models (LLMs) - prompt engineering, fine-tuning, or RAG-based architectures
• Exposure to Generative AI tools and frameworks - LangChain, LlamaIndex, OpenAI API, or similar
• Experience with computer vision or NLP-specific pipelines
• Familiarity with vector databases - Pinecone, Weaviate, ChromaDB, or similar
• Knowledge of containerization and deployment - Docker, Kubernetes, or Azure ML
• Understanding of data pipelines and feature stores in an ML context
• Experience working in Agile or sprint-based delivery environments

Who Will Thrive in This Role

Someone who is genuinely excited about AI - not just as a buzzword, but as an engineering discipline. You read papers, experiment on weekends, and get frustrated when a model makes it to production without proper evaluation. If you believe good AI is 20% modeling and 80% everything else around it - you ll fit right in.