Machine Learning Engineer

Job highlights

• Proficient in Python,NLP knowledge and practical experience in solving NLP problems in areas such as text classification,entity tagging,information retrieval,question-answering,natural language generation,clustering,etc

• Knowledge and experience with data engineering,basic machine learning concepts,data mining,feature extraction,pattern recognition,etc

• Big picture: Understand customers needs and innovate and use cutting-edge Machine Learning techniques to build data-driven solutions.

• Work on NLP problems across areas such as text classification, entity extraction, summarisation, generative NLP, and others.

• Collaborate with cross-functional teams to integrate/upgrade AI solutions into company s products and services Optimise existing machine learning models for performance, scalability and efficiency.

• Build, deploy and own scalable production NLP pipelines.

• Build post-deployment monitoring and continual learning capabilities.

• Propose suitable evaluation metrics and establish benchmarks.

• Keep abreast of SOTA techniques in your area and exchange knowledge with colleagues.

• Desire to learn, implement and apply latest emerging model architectures (like LLMs), inference optimizations, distributed training, using open-source models, etc.

Well love to explore more about you if you have:

• Bachelors in Computer Science or mathematics-related fields with 2+ years of experience in Machine Learning and NLP.
• Proficient in Python, NLP knowledge and practical experience in solving NLP problems in areas such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc.
• Knowledge and experience with data engineering, basic machine learning concepts, data mining, feature extraction, pattern recognition, etc.
• Knowledge and hands-on experience with Transformer-based Language Models like BERT, DeBERTa, Flan-T5, GPT, Llama, Gemma, DeepSeek, etc.
• Deep familiarity with Model Training concepts, model inference optimisations, GPUs, etc.
• Experience with Deep Learning frameworks like Pytorch and common machine learning libraries like scikit-learn, numpy, pandas, NLTK, transformers, etc.
• Experience with ML model deployments using REST API, Docker, Kubernetes, etc.
• Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable.
• Knowledge of basic Data Structures and Algorithms.
• Knowledge of Multimodal models is a plus
• Knowledge of real-time streaming tools/architectures like Kafka, Pub/Sub is a plus.