Machine Learning Engineer

Key Responsibilities

Model Development & Implementation

• Design, train, and fine-tune machine learning and deep learning models for NLP, computer vision, and predictive analytics tasks.
• Work with frameworks like TensorFlow, PyTorch, and Scikit-learn for building and deploying ML models.
• Assist in developing RAG (Retrieval Augmented Generation) pipelines and LLM-based applications under senior supervision.
• Implement and maintain APIs using frameworks like FastAPI, Flask, or Django for ML model integration.

Data Handling & Preprocessing

• Perform data cleaning, feature extraction, and transformation for model readiness.
• Contribute to building and maintaining data pipelines for training and inference stages.

Deployment & MLOps

• Containerize ML solutions using Docker and participate in deploying models to production environments.
• Support MLOps workflows, version control using DVC, and CI/CD pipelines for continuous delivery.

Research & Collaboration

• Stay updated with the latest trends in AI, LLMs, and computer vision.
• Collaborate with cross-functional teams, data engineers, and senior ML engineers to ensure robust system integration.
• Document workflows, experiments, and technical findings clearly.

Qualifications

Education:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related discipline.

Experience:

• 2–3 years of hands-on experience in machine learning model development and deployment.
• Strong understanding of fundamental ML algorithms and their mathematical background.
• Exposure to NLP, Computer Vision, or LLM-based systems is preferred.

Technical Skills:

• Proficient in Python and familiar with libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch.
• Knowledge of LLMs, LangChain, or RAG frameworks is a plus.
• Experience with RESTful APIs and backend integration.
• Familiarity with Git, Docker, and cloud platforms (AWS, GCP, or Azure).

Nice to Have

• Experience with LoRA, QLoRA, or model fine-tuning techniques.
• Familiarity with image classification, object detection, or segmentation tasks.
• Exposure to auto-scaling, model optimization, or embedded ML applications.
• Interest in learning advanced AI concepts like multi-agent systems, meta-learning, and generative AI.

Job Type: Full-time

Application Question(s):

• Did you have experience with Computer vision, LLM, nlp and Audio?

Experience:

• Machine Learning : 2 years (Required)

Work Location: In person