AI/ML Engineer

Job summary

Design, train, optimize, and deploy machine learning models and AI-powered applications, including solutions using generative AI, LLMs, and RAG where applicable. Build data pipelines, integrate and monitor models, evaluate their performance, and collaborate with cross-functional teams to deliver AI solutions that meet business needs.

Job details

Key Responsibilities Design, develop, train, and optimize machine learning models. Collect, clean, preprocess, and analyze structured and unstructured data. Apply supervised, unsupervised, and deep learning techniques to solve business problems. Develop AI-powered applications using machine learning and generative AI technologies. Work with Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG), where applicable. Build and deploy ML models using APIs, cloud platforms, and containerization tools. Evaluate model performance using appropriate metrics and improve accuracy, efficiency, and scalability. Develop data pipelines and integrate AI/ML models into existing applications. Monitor deployed models, detect performance degradation, and maintain model quality. Collaborate with cross-functional teams to translate business requirements into AI solutions. Maintain technical documentation and follow software engineering best practices. Required Skills Strong programming knowledge in Python. Experience with machine learning libraries such as Scikit-learn, TensorFlow, or PyTorch. Knowledge of machine learning algorithms, statistics, probability, and linear algebra. Experience with data manipulation libraries such as Pandas and NumPy. Familiarity with SQL and databases. Understanding of model evaluation, feature engineering, and hyperparameter tuning. Knowledge of REST APIs, Git, and software development practices. Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud is an advantage. Preferred Skills Experience with Generative AI, LLMs, LangChain, or LlamaIndex. Knowledge of vector databases such as FAISS, Pinecone, or Chroma. Experience with Docker, Kubernetes, MLflow, or similar MLOps tools. Understanding of CI/CD pipelines and model monitoring. Experience with NLP, computer vision, recommendation systems, or time-series forecasting. Familiarity with responsible AI, model explainability, and data privacy.

What you’ll do

Design, train, optimize, and deploy machine learning models and AI-powered applications, including solutions using generative AI, LLMs, and RAG where applicable. Build data pipelines, integrate and monitor models, evaluate their performance, and collaborate with cross-functional teams to deliver AI solutions that meet business needs.

Requirements

Requires strong Python programming skills, familiarity with machine learning libraries, and knowledge of algorithms, statistics, probability, and linear algebra. Also requires experience with data manipulation, SQL, model evaluation, feature engineering, hyperparameter tuning, REST APIs, Git, and software development practices; cloud, generative AI, vector databases, and MLOps experience are advantageous.

Listed skills

• Python · Preferred
• Machine learning · Preferred
• SQL · Preferred
• REST APIs · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• Machine Learning
• Scikit-Learn
• TensorFlow
• PyTorch
• Statistics
• Probability
• Linear Algebra
• Pandas
• NumPy
• SQL
• Feature Engineering
• Hyperparameter Tuning
• REST APIs
• Generative AI
• Large Language Models

Job areas

• Technology
• Software
• Data & Analytics
• Engineering

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