AI Engineer / Data Scientist

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