Software Engineer - Machine Learning

Roles and Responsibilities :

• Big picture: Understand customers’ needs and innovate and use cutting edge Machine Learning techniques to build data-driven solutions.
• Collaborate with cross-functional teams to integrate/upgrade AI solutions into company’s products and servicesContribute in achieving high engineering standards across different services.
• Optimize existing machine learning models or 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 with SOTA techniques in your area and exchange knowledge with colleagues.
• Suggest impactful improvements and own the post-deployment MLOps service activities completely.
• Contribute in prompt engineering efforts within the team.
• Active participation in bugs identification, resolution, RCA, testing, etc.

We'll love to explore more about you if you have :

• Bachelors in Computer Science or Mathematics related fields with 2-3 years of experience in Machine Learning, NLP and Software Engineering.
• Proficient in Python and SQL.
• Knowledge of basic Data Structures and Algorithms, and proficient in coding.
• Knowledge and experience with data engineering, basic machine learning concepts, data mining, feature extraction, pattern recognition, etc.
• Knowledge and experience with dashboards, monitoring and logging tools like datadog, grafana, etc.
• Basic understanding of NLP problems such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc.
• Basic knowledge and hands-on experience with Transformer-based Language Models like BERT, DeBERTa, Flan-T5, GPT, Llama, etc.
• Experience with Deep Learning frameworks like Pytorch and common machine learning libraries like scikit-learn, numpy, pandas, NLTK, etc.
• Experience with prompt engineering and streamlining related processes.
• Experience in following clean, elegant, modular, bug-free coding practices in languages like Python.
• Experience with ML model deployments using REST API, Docker, Jenkins, Helm, Kubernetes, etc.
• Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable.
• Knowledge of real-time streaming tools/architectures like Kafka, Pub/Sub is a plus.