CommerceIQ - Data Science Manager - AWS SageMaker

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:

- Masters 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.

(ref:hirist.tech)