AI/ML Engineer

Skills: Python, Machine Learning, Deep Learning, Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy, SQL, NLP, Computer Vision, Generative AI, LLM, Prompt Engineering, RAG, Vector Databases, REST APIs

Responsibilities:

• Design, develop, and deploy Machine Learning and AI solutions for business applications.
• Build and optimize ML models for classification, regression, forecasting, recommendation, and NLP use cases.
• Develop data preprocessing, feature engineering, model training, and evaluation pipelines.
• Work with Python, Pandas, NumPy, Scikit-learn, TensorFlow, and/or PyTorch.
• Develop and integrate Generative AI and LLM-based solutions where applicable.
• Work with OpenAI/LLM APIs, prompt engineering, embeddings, vector databases, and RAG architectures.
• Build scalable ML pipelines using MLflow, Kubeflow, Databricks, AWS, Azure, or Google Cloud Platform.
• Deploy models through REST APIs, Docker, Kubernetes, and cloud platforms.
• Monitor model performance, data quality, drift, and production issues.
• Collaborate with Data Engineers, Software Engineers, Data Scientists, Product Owners, and business stakeholders.
• Perform model tuning, experimentation, validation, and performance optimization.
• Implement MLOps practices for CI/CD, model versioning, experiment tracking, and automated deployment.
• Ensure AI solutions meet requirements for security, scalability, reliability, and responsible AI.
• Document models, architectures, workflows, and technical processes.

Required Skills

• Python
• Machine Learning
• Deep Learning
• Scikit-learn
• TensorFlow / PyTorch
• Pandas / NumPy
• SQL
• NLP / Computer Vision as applicable
• Generative AI / LLM
• Prompt Engineering
• RAG
• Vector Databases
• REST APIs
• Docker / Kubernetes
• Cloud: AWS / Azure / Google Cloud Platform
• Git
• MLOps / MLflow