Roles and Responsibilities

• Design, build, and deploy machine learning models and AI-enabled solutions using frameworks such as PyTorch, TensorFlow, MXNet, or Keras.
• Develop Natural Language Processing (NLP), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) solutions for enterprise applications.
• Build and maintain API services for serving ML/NLP models using Flask, FastAPI, or similar frameworks.
• Implement and manage model deployment pipelines using Docker, Kubernetes (K8s), and CI/CD tools such as GitLab CI/CD.
• Analyze large-scale datasets and apply machine learning techniques such as clustering, regression, and model evaluation for insight extraction and solution development.
• Collaborate with data scientists, software engineers, and product teams to integrate ML models into production systems.
• Solve complex and unbounded technical problems with innovative and scalable solutions.
• Stay updated with the latest trends in AI, ML, and deep learning technologies.

Required Qualifications

• Bachelor’s degree in Computer Science, Mathematics, or a related field.
• Strong programming experience in Python and familiarity with AI/ML libraries and frameworks.
• Practical experience in building and deploying NLP and LLM models.
• Familiarity with RAG systems and integrating them into real-world applications.
• Proficiency in containerization and orchestration tools (Docker, Kubernetes).
• Experience with CI/CD pipelines, especially using GitLab or similar tools.
• Solid understanding of AI/ML concepts such as algorithm selection, model evaluation, and optimization.
• Strong problem-solving skills and ability to work in a fast-paced, evolving environment.

Preferred Qualifications

• Master’s degree in a related field.
• Experience with distributed systems and big data platforms (e.g., Spark, Hadoop).
• Exposure to cloud platforms (AWS, Azure, GCP) for model training and deployment.
• Experience with monitoring and maintaining deployed ML models.

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