Ai Ml Engineer

Job Title:

About the Role:

Senior AI/ML Engineer

Key Responsibilities:

• Architect, design, and deploy advanced

  AI/ML and deep learning models

  for NLP, computer vision, and multi-modal use cases.
• Build

  end-to-end ML pipelines

  covering data engineering, preprocessing, model training, evaluation, and deployment.
• Implement

  fine-tuning techniques

  (LoRA, parameter-efficient tuning, transfer learning, etc.) for LLMs and domain-specific tasks.
• Leverage the

  Hugging Face ecosystem

  for model development, training, and deployment.
• Work extensively with

  Python and ML/DL frameworks

  (PyTorch, TensorFlow, scikit-learn).
• Drive adoption of

  MLOps best practices

  (CI/CD for ML, MLflow, Kubeflow, Airflow).
• Collaborate with cross-functional teams to integrate AI models into production-grade applications.
  
• Mentor junior engineers and establish

  best practices for scalable ML engineering

  .
• Research and apply state-of-the-art advancements in

  LLMs, transformers, generative AI, and fine-tuning

  .

Required Skills & Qualifications:

• Bachelor’s/Master’s degree in

  Computer Science, Data Science, AI, or related field

  .
• 8+ years of hands-on experience in

  AI/ML development and deployment

  .
• Expert in

  Python programming

  and ML/DL frameworks (PyTorch, TensorFlow).
• Strong understanding of

  machine learning algorithms, deep learning architectures, transformers, and NLP techniques

  .
• Proven experience with

  Hugging Face Transformers, LoRA, and fine-tuning large language models (LLMs)

  .
• Experience with

  cloud platforms (AWS, Azure, GCP)

  and

  containerization

  (Docker, Kubernetes).
• Solid knowledge of

  data preprocessing, feature engineering, and production model deployment

  .

Preferred Skills:

• Expertise in

  parameter-efficient fine-tuning (PEFT)

  and

  generative AI

  workflows.
• Exposure to

  big data ecosystems

  (Spark, Hadoop) for large-scale ML applications.
• Experience in

  multi-modal AI systems

  (text, image, and audio).
• Strong foundation in

  statistics, optimization, and applied mathematics

  .
• Experience leading teams and defining

  AI strategy & best practices

  .