AI/ML engineer

Key skills

What you’ll do

• Design, develop, and deploy machine learning and generative AI solutions to solve business problems. Responsibilities include building data pipelines, fine-tuning foundation models, and implementing MLOps best practices.

What they’re looking for

• Requires a degree in Computer Science or a related field and proficiency in Python, deep learning frameworks, and cloud platforms. Experience with LLMs, RAG architectures, and vector databases is essential.

Job description

Role overview Design, develop, deploy, and optimize machine learning and generative AI solutions that solve business problems and create measurable impact. Key responsibilities Build, train, evaluate, and deploy ML models. Develop data pipelines and feature engineering workflows. Work with NLP, LLMs, computer vision, or predictive analytics solutions. Fine-tune foundation models and implement RAG architectures. Collaborate with product, engineering, and business teams. Monitor model performance and implement MLOps best practices. Conduct experimentation and model benchmarking. Required Skills & Qualifications Python (NumPy, Pandas, Scikit-learn). Machine learning and deep learning fundamentals. PyTorch and/or TensorFlow. SQL and data processing. Cloud platforms (Azure, AWS, or GCP). Git, CI/CD, and MLOps concepts. LLMs, prompt engineering, vector databases, and RAG. Education Bachelor's or master's degree in Computer Science, AI, Data Science, Statistics, or related field. Preferred Skills Experience with Azure AI, OpenAI, LangChain, Semantic Kernel, or similar frameworks. Exposure to Docker and Kubernetes. Knowledge of model deployment and monitoring. Success metrics High-quality model performance, scalable deployments, strong stakeholder collaboration, and continuous improvement of AI solutions.

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