~2mo ago

AI Engineer (Indian Candidates)

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Machine Learning EngineerData

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Quick Summary

Overview

Design, develop, train, evaluate, and optimize machine learning and deep learning models. Perform feature engineering, data preparation, and dataset validation to improve model performance.

Technical Tools

Machine Learning EngineerData

Design, develop, train, evaluate, and optimize machine learning and deep learning models.
Perform feature engineering, data preparation, and dataset validation to improve model performance.
Translate business and technical requirements into measurable machine learning objectives and success metrics.
Conduct experiments, benchmarking, and model evaluations to validate and improve ML solutions.
Develop modular and reusable Python pipelines for data preprocessing, feature extraction, model training, and inference.
Deploy machine learning models as scalable APIs or microservices with proper versioning and experiment tracking.
Build and maintain automated training and inference workflows while monitoring model accuracy, latency, and drift in production environments.
Optimize model inference and data pipelines for performance using CPU/GPU optimization techniques such as batching,mixed precision (FP16), and parallel processing.
Containerize machine learning applications and implement CI/CD pipelines to support reliable deployments.
Design and maintain ETL pipelines for processing structured and unstructured data, including image, audio, and video datasets.
Implement logging, monitoring, and alerting to ensure the reliability, scalability, and continuous improvement of ML services.
Collaborate

Requirements

~1 min read

Minimum 2 years of experience in AI, Machine Learning, or Deep Learning development.
Strong knowledge of supervised and unsupervised learning, deep learning, and transfer learning techniques.
Experience working with CNNs, RNNs, Transformers, computer vision, and multimodal AI models.
Proficiency in Python programming and Bash scripting.
Experience developing REST APIs using FastAPI or Flask.
Hands-on experience with PyTorch, TensorFlow, Scikit-learn, NumPy, Pandas,OpenCV, Librosa, Hugging Face, OpenCLIP, and TorchVision.
Familiarity with Docker, GitHub, CI/CD pipelines, MLflow, Weights & Biases, ONNX, and TorchScript.
Working knowledge of Linux environments, SSH, GPU utilization, multiprocessing, and basic AWS cloud services.
Experience designing scalable machine learning systems and deploying production-ready ML microservices.
Strong debugging, troubleshooting, and performance optimization skills.
Ability to write clean, maintainable, and scalable code following software engineering best practices.
Strong analytical, problem-solving, and experimentation skills.
Excellent technical documentation, communication, and collaboration skills.
Understanding of Agentic AI concepts, including AI agents, workflow orchestration,tool integration, and autonomous task execution using modern LLMframeworks.
Experience working directly with clients to understand business requirements and translate them into practical, scalable AI/ML solutions.