AI Engineer / Data Scientist

C3i Hub

2 - 3 years

10 - 14 Lacs

kanpur

Posted:5 days ago| Platform:

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Skills Required

computer science deep learning prototype gcp debugging machine learning performance optimization model development monitoring python

Work Mode

Work from Office

Job Type

Full Time

Job Description

You ll work hands-on with large datasets, cutting-edge frameworks, and MLOps practices to deliver production-grade AI systems
- 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, andaugmentation techniques
Conduct hyperparameter optimization and experiment management
- Evaluation & Optimization Run model evaluations, benchmark against baselines, and perform ablationstudies
Optimize models for accuracy, latency, and resource efficiency (eg, 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
- Bachelor s/Master s degree in Computer Science, IT, Mathematics, Statistics, orrelated 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 modelregistries
- Familiarity with cloud platforms (AWS/GCP/Azure) for training and deployment
- Strong debugging, profiling, and performance optimization skills

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