Data Scientist

Role & responsibilities

Job Summary:

Machine Learning Engineer

Key Responsibilities:

• Build, train, and optimize supervised and unsupervised learning models.
• Work on deep learning techniques, including

  LSTM

  , and use frameworks like

  TensorFlow

  and

  PyTorch

  , including

  GPT

  models.
• Apply advanced

  NLP

  techniques and tools (e.g.,

  SpaCy, LLMs

  ) for analyzing and processing unstructured data.
• Develop and fine-tune models for real-world applications and integrate them into production systems.
• Continuously monitor and improve the performance and quality of deployed models.
• Collaborate with cross-functional teams and stay updated on the latest enhancements in ML, AI, and NLP technologies.

Required Skills & Qualifications:

• Experience:

  4+ years of total experience, with 2+ years specifically in ML model deployment.

• Programming:

  4+ years of hands-on experience in

  Python

  with strong coding and debugging skills.

• Deep Learning Focus:

  Experience in

  Computer Vision, OCR, NLP, LLM, and LangChain

  .

• Data Proficiency:

  Experience in handling large, complex datasets and proficiency in

  SQL

  queries.

• Visualization:

  Good understanding of data visualization using tools like

  Power BI

  or

  Tableau

  .

• Education:

  Bachelors in Computer Science, Engineering, or a related field.

Preferred candidate profile

• Cloud Deployment Expertise:

  Hands-on experience deploying and managing ML models in a

  production environment

  using cloud services (e.g.,

  AWS Sagemaker, Azure ML, or Google AI Platform

  ).

• MLOps Principles:

  Familiarity with MLOps best practices, including versioning (data, code, models), experiment tracking (e.g., MLflow), and automated deployment pipelines.

• Architecture:

  Experience in designing scalable model serving architectures for low-latency inference.

• Domain Adaptation:

  Proven ability to quickly adapt and apply ML solutions to new and diverse business domains.

• Advanced NLP:

  Experience fine-tuning and customizing open-source

  Large Language Models (LLMs)

  beyond basic usage.