AI Engineer

5 days ago

kanpur, India Acura Solution Full time

Job Description:Key Responsibilities  Model Development & Training ○ Design, train, and fine-tune deep learning and machine learning models (transformers, CNNs, RNNs, gradient boosting, etc.). ○ Implement data preprocessing pipelines, feature extraction, and augmentation techniques. ○ Conduct hyperparameter optimization and experiment management. ● Evaluation & Optimization ○ Run model evaluations, benchmark against baselines, and perform ablation studies. ○ Optimize models for accuracy, latency, and resource efficiency (e.g., pruning, quantization). ○ Apply error analysis to improve robustness and generalization.  Deployment & MLOps ○ Package and deploy models into production using containerization (Docker/Kubernetes). ○ Integrate models into APIs, microservices, or larger platforms. ○ Set up monitoring and automated retraining pipelines for live systems.  Stay updated with emerging research and frameworks (transformer architectures, foundation models, multimodal learning).  Experiment with adapting state-of-the-art models to practical business problems. ● Prototype novel approaches and validate them against real datasets. Required Qualifications  Bachelor's/Master's degree in Computer Science, IT, Mathematics, Statistics, or related discipline (or strong practical experience in AI/ML).  2-3+ years of professional experience in AI/ML engineering or applied data science. ● Proficiency in Python and ML libraries (NumPy, Pandas, scikit-learn). ● Strong expertise in deep learning frameworks: PyTorch (preferred) or TensorFlow. ● Experience with transformers and pretrained models (Hugging Face ecosystem, BERT, GPT, etc.). Solid understanding of ML fundamentals: optimization, probability, linear algebra, statistics.  Hands-on experience with large-scale datasets and distributed training workflows. ● Knowledge of MLOps tools such as MLflow, Weights & Biases, Docker, or model registries.  Familiarity with cloud platforms (AWS/GCP/Azure) for training and deployment. ● Strong debugging, profiling, and performance optimization skills. Preferred Skills  Experience with self-supervised learning, reinforcement learning, or graph neural networks.  Knowledge of distributed computing frameworks (Ray, Spark, Horovod). ● Exposure to multimodal learning (vision + text, speech + text).  Understanding of software engineering best practices: version control, CI/CD, testing. ● Contributions to open-source ML projects or relevant publications.  Strong presentation skills to explain technical trade-offs to peers and stakeholders

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