Machine Learning Engineer - LLM / Deep Learning

Role : Machine Learning Engineer

Location : Bangalore office (preferable)

YOE : 5-10 years

Company Overview :

Neutrinos enable financial services and insurance enterprises to quickly turn ideas into business outcomes with our AI-powered hyper-automation platform, a library of purpose-built low-code solutions, and deep industry expertise.

Enterprises globally rely on Neutrinos' platform to re-imagine, develop, and deploy complex, process driven, and customer-engaging business applications. We currently work with 30+ leading enterprises over multiyear engagements across the globe as their long-term strategic partner across various use cases.

Started in 2015, headquartered in Singapore, we are currently present in S Africa, Singapore, Australia and India. Globally recognized among top 18 Voice of Customer Award by Gartner recently

JD :

Role : Machine Learning Engineer Location : Bangalore office (preferable)

• Fundamental knowledge of supervise and unsupervised learning and their applications in real world business scenarios.
• Familiarity with End-2-End Machine Learning model development life cycle.
• Experience in creating data processing pipelines and API development (Fast API).
• Experience with deploying and maintaining ML / DL models in production, model monitoring, and knowledge of concept / data drift, model reproducibility, code, model, and data versioning.
• Experience with SQL and NoSQL, MLflow, GitHub, docker, Kubernetes, ELK, or similar stack.
• Hands on with text and image processing : cleaning, transformations, and data preparation for modelling. And should be comfortable with libraries like Pandas, NumPy, OpenCV, PIL, Spacy, transformers etc.
• Should have working knowledge of machine learning frameworks like Scikitlearn, PyTorch / Tensorflow / Keras.
• Experience with cloud computing platforms like GCP and AWS.
• Should understand LLMs, Open AI API, model fine-tuning and prompt engineering.
• Exposure to CI / CD principles and associated tools.

Good to have :

Onsite - 5 days, HSR, Bangalore ( 4 days WFH in a month ).

ref : hirist.tech)