Summary
✨ AI‑Generated
An AI/ML engineering opportunity focused on delivering production-grade machine learning solutions for real-world business problems. Responsibilities span model development and evaluation, generative AI and LLM applications, retrieval-augmented generation, intelligent agents, cloud infrastructure, APIs, containers, and MLOps.
Highlights
End-to-end AI/ML ownership spanning model development, generative AI, LLMs, RAG, intelligent agents, production deployment, and MLOps, with opportunities to solve practical business problems.
Description
Key Responsibilities
Design, develop, train, and deploy machine learning models for real-world business problems.Build end-to-end ML pipelines from data preparation through model deployment and monitoring.Develop AI/ML solutions using Python and modern machine learning frameworks.Apply supervised, unsupervised, and deep learning techniques to solve complex problems.Develop and integrate Generative AI and Large Language Model (LLM) applications.Build Retrieval-Augmented Generation (RAG) systems using embeddings and vector databases.Develop AI agents and intelligent automation solutions where appropriate.Fine-tune and evaluate machine learning and language models.Implement data preprocessing, feature engineering, model selection, and hyperparameter optimization.Develop APIs and services to integrate ML models into production applications.Build scalable ML infrastructure using cloud platforms and containerized environments.Implement MLOps practices for model versioning, deployment, monitoring, and lifecycle management.Monitor model performance, accuracy, latency, reliability, and data/model drift.Collaborate with software engineers to integrate AI capabilities into existing products and platforms.Conduct experiments, analyze results, and communicate findings to technical and non-technical stakeholders.Follow best practices for data privacy, security, responsible AI, and model governance.Stay current with emerging AI/ML technologies, frameworks, and research.Required Technical Skills
Programming & Machine Learning
Strong proficiency in Python.Experience with PyTorch, TensorFlow, or Keras.Strong knowledge of Scikit-learn.Experience with NumPy, Pandas, SciPy, and related Python libraries.Strong understanding of machine learning algorithms and statistical concepts.Experience with model training, evaluation, optimization, and deployment.
