AI/ML Engineer

About the role

Winaxis LLC is looking for an inventive AI/ML Engineer to build intelligent systems that address practical business challenges. This position involves creating, training, and launching machine learning and artificial intelligence solutions, with a strong emphasis on data handling, model operationalization, and cloud-based environments.

What you'll do

• Design, train, tune, and improve machine learning and deep learning models.
• Create and support data pipelines that can scale for both training and inference workflows.
• Develop AI-driven products using natural language processing, computer vision, generative AI, and predictive analytics.
• Take models from development into production using MLOps-oriented deployment practices.
• Work with large datasets, including preprocessing, feature creation, and model validation.
• Partner with data engineers, software developers, and business teams to translate requirements into AI solutions.
• Track model behavior over time and introduce ongoing enhancements.
• Investigate new AI tools, frameworks, and market trends to stay current with the field.
• Create APIs and microservices that allow AI models to connect with other systems.
• Maintain standards around data protection, model governance, and compliance.

Required background

A bachelor's or master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or a similar discipline is expected. Strong Python programming ability is essential, along with hands-on experience using machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, and XGBoost. Candidates should have a solid grasp of supervised and unsupervised learning, deep learning, neural networks, NLP, computer vision, and preferably reinforcement learning.

The role also calls for experience with SQL and NoSQL databases, plus familiarity with deployment tools like Docker, Kubernetes, and MLflow. Exposure to cloud platforms such as AWS, Azure, or GCP is important, and working knowledge of Git is needed. Additional value comes from experience with generative AI and large language models, including tools and frameworks like LangChain, LlamaIndex, Hugging Face, OpenAI APIs, and vector databases such as Pinecone, Weaviate, ChromaDB, or FAISS. Prior work with RAG implementations, MLOps, CI/CD pipelines, Databricks, and Apache Spark is also preferred.

Skills and strengths

• Python programming
• Machine learning model development
• Deep learning
• Data preprocessing and feature engineering
• NLP and computer vision
• Model deployment and MLOps
• SQL and NoSQL databases
• Cloud platform experience
• Git version control
• API and microservice development
• Analytical thinking and problem solving
• Cross-functional collaboration

Nice-to-have experience

• Generative AI and LLM development
• AI agent and multi-agent system work
• Prompt engineering
• LLM fine-tuning
• Knowledge graphs
• RAG solutions
• Databricks and Apache Spark
• MLOps certification
• Cloud certifications in AWS, Azure, or GCP