Associate AI/ML Engineer

About the Role

This role is ideal for junior engineers who are passionate about AI/ML, eager to learn, and excited to work with modern technologies such as NLP and Large Language Models under the guidance of senior team members.

Key Responsibilities

• Assist in researching and implementing machine learning and NLP techniques including text classification, summarization, sentiment analysis, and named entity recognition
• Support experimentation with Large Language Models such as GPT, BERT, and LLaMA for business and product use cases
• Apply prompt engineering techniques to improve LLM outputs for various applications
• Help fine-tune, evaluate, and optimize open-source or proprietary models for specific applications
• Participate in data collection, cleaning, preprocessing, and annotation to improve model accuracy
• Assist in building and maintaining machine learning and NLP pipelines, APIs, and integrations
• Conduct model testing, performance evaluation, and error analysis and suggest improvements
• Collaborate with product managers, engineers, and data teams to deliver AI-powered features
• Document experiments, models, and technical workflows
• Stay updated with advancements in AI, machine learning, NLP, and LLM technologies

Requirements & Tech Stack

• Strong fundamentals in Python programming
• Basic understanding of machine learning concepts and model evaluation
• Hands-on exposure to deep learning frameworks such as TensorFlow or PyTorch
• Prompt engineering experience for Large Language Models
• Familiarity with NLP libraries and tools such as spaCy, NLTK, Hugging Face Transformers, OpenAI APIs, or LangChain
• Understanding of data preprocessing, feature engineering, and basic statistics
• Experience using Git or other version control systems
• Strong problem-solving skills and eagerness to learn

Qualifications

• Bachelor’s degree (ongoing or completed) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field

Preferred Qualifications

• Knowledge of vector embeddings, semantic search, or retrieval-augmented generation concepts
• Experience building or consuming REST APIs
• Exposure to Docker, Kubernetes, or MLOps concepts
• Familiarity with cloud platforms such as AWS, GCP, or Azure
• Internship, academic projects, or Kaggle competition experience