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:

• (Preferred) Proven ability to lead and mentor a junior 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.

Education & Experience:

• 3+ years of hands-on experience in applied machine learning and data science with Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field or appropriate experience.
• 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.

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.
• 5+ years of hands-on experience in applied machine learning and data science.

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.