Technical Expertise
- Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc.).
- Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.
- Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem and good to have distributed training frameworks (e.g., DeepSpeed, PyTorch Lightning, Ray).
- Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
- Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
- Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.
- Applied Problem-Solving
Mandatory skill -
- Demonstrated success in adapting foundation models to domain-specific applications through fine-tuning or transfer learning.Mandatory skill -
- Strong ability to design, evaluate, and improve models using robust validation strategies, bias/fairness checks, and performance optimization techniques.
- Experience in working on applied AI problems across NLP, computer vision, or multimodal systems or any other domain.
Leadership & Collaboration
- Proven ability to lead and mentor a team of applied scientists and ML engineers, providing technical guidance and fostering innovation.
- Strong cross-functional collaboration skills to work with product, engineering, and business stakeholders to deliver impactful AI solutions.
- Ability to translate cutting-edge research into practical, scalable solutions that meet real-world business needs.
Other
- Excellent communication and presentation skills to articulate complex ML concepts to both technical and non-technical audiences.
- Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques.
Education & Experience
- Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field.
- 7+ years of hands-on experience in applied machine learning and data science, with at least 2+ years in a leadership or managerial role.
