Senior Machine Learning Engineer - Timeseries analysis

Important Details

This is Remote Part-Time role and we expect around 12-16 hours of work per week.

At times requires travel to client location - costs reimbursed (at max once per month).

Pay per month ranges from 40k to 60k (fully based on expertise how well you complete tasks, not experience)

Contract shall be at least for 6months. We are indeed looking for long-term collaboration. Hence can be made full time.

You should have done : Timeseries Analysis (linear & non-linear), Optimisation (gradient-based and gradient-free), Gaussian Process Regression. (GenAI expertise is a plus).

You should be keen about technology, self-disciplined (work with minimal monitoring), take initiatives to help build the product.

Company Description

V-OptimAIse aims to leverage AI / ML advancements to reduce carbon footprint in steel manufacturing by developing self-learning predictive and diagnostic tools. Our team utilizes under-utilized data to identify production bottlenecks, detect product anomalies, and avoid critical failures. These efforts are geared towards maximizing productivity and improving operational efficiency in steel manufacturing.

Role Description

This is a part-time remote role for a Senior Machine Learning Engineer specialized in Timeseries Analysis & Generative AI. The Senior Machine Learning Engineer will be responsible for developing and implementing machine learning models for prediction, anomaly detection and non-linear optimisation. Further they develop GenAI tools for providing valid recommendations based on the model output.

Daily tasks also include analyzing timeseries data, optimizing algorithms, building GenAI tools capable of helping industrial operators and collaborating with cross-functional teams, developing web-apps based in Streamlit. Also involves developing GenAI tools and leveraging Agentic AI over the app.

Qualifications

Expertise in timeseries analysis

Strong experience applying GenAI, LLMs and Agentic AI for product based platforms

Gaussian Processes (or VGPs), TCNN, VAE etc

Python for data analysis, model development

GPflow, PyTorch, Bayesian optimisation techniques

Strong foundation in Computer Science and Algorithms

Proficiency in Statistics and Data Analysis

Excellent problem-solving and critical thinking skills

Ability to work independently and remotely

Experience in AI / ML applications in manufacturing is a plus

Masters or advanced degree in Computer Science, or related field