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 skilled 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

📌 AI Engineer / Data Scientist (Kanpur)
🏢 Acura Solution
📍 Kanpur