Senior Machine Learning Engineer - Time Series Forecasting

About the Role :

We are looking for a highly skilled Senior Machine Learning Engineer - Forecasting to build scalable and high-performance machine learning solutions focused on time series forecasting and predictive modeling.

The ideal candidate should possess strong expertise in machine learning, statistical modeling, deep learning, and time series forecasting techniques, along with hands-on experience building production-grade ML systems using modern Python-based ML frameworks.

This role requires someone who is research-oriented, capable of exploring state-of-the-art literature, implementing advanced forecasting models, and innovating new machine learning approaches at industry standards.

Candidates with exposure to NLP, Computer Vision, GPU-based training systems, and research publication experience will be strongly preferred.

Key Responsibilities :

Machine Learning & Forecasting :

- Design, develop, and maintain scalable machine learning systems for :

i. Time Series Forecasting

ii. Predictive Modeling

iii. Statistical Forecasting

iv. Deep Learning-based Forecasting

- Build and optimize forecasting models across both CPU and GPU environments.

- Work on :

i. Statistical & Probabilistic Models

ii. Transformer-based Forecasting Models

iii. Deep Learning Architectures

iv. Sequential Prediction Systems

- Evaluate model performance and continuously improve forecasting accuracy and scalability.

Research & Innovation :

- Conduct exploratory data analysis (EDA) and derive meaningful insights from complex datasets.

- Read, evaluate, and implement state-of-the-art machine learning research papers and forecasting methodologies.

- Experiment with novel ML architectures and advanced forecasting techniques.

- Contribute to innovation initiatives and research-grade model development.

- Potentially contribute to international conferences, technical publications, or industry-standard research initiatives.

Model Development & Engineering :

- Build production-grade ML pipelines and scalable model training workflows.

- Develop reusable ML components, experimentation frameworks, and evaluation pipelines.

- Write clean, efficient, and maintainable Python code for production environments.

- Collaborate with platform and engineering teams to deploy and monitor ML systems in production.

Cross-functional Collaboration :

- Work closely with :

i. Data Scientists

ii. ML Engineers

iii. Product Teams

iv. Platform Engineering Teams

to develop scalable AI/ML solutions.

- Support model deployment, experimentation, monitoring, and optimization activities.

Required Skills & Qualifications :

- 4-6 years of experience in :

i. Machine Learning

ii. Predictive Modeling

iii. Time Series Forecasting

iv. AI/ML Engineering

- Strong experience working with :

i. Time Series Forecasting Problems

ii. Statistical Modeling

iii. Deep Learning

iv. Transformer Architectures

- Hands-on expertise in :

i. Python

ii. PyTorch

iii. Scikit-learn

iv. Time Series Python Libraries

- Strong understanding of :

i. Forecasting Algorithms

ii. Probabilistic Models

iii. Model Evaluation Techniques

iv. ML Experimentation Frameworks

- Experience writing production-level Python code.

- Strong foundations in :

i. Data Structures

ii. Algorithms

iii. System Design

iv. Statistical Methods

- Experience with :

i. CPU/GPU model training environments

ii. Scalable ML systems

iii. ML experimentation workflows

Technical Skills :

- Programming Languages : Python

- ML/DL Frameworks : PyTorch, Scikit-learn, Transformers

- Time Series & Statistical Libraries : Pandas, NumPy, Statsmodels, Prophet, Darts, GluonTS (preferred)

- Deep Learning & Forecasting : LSTM, Transformer Models, Temporal Fusion Transformers, Sequence Modeling

- Data & Infrastructure : SQL, GPU Training Environments, ML Pipelines, Experiment Tracking Tools

- Exposure to : NLP, Computer Vision, MLOps Concepts, Cloud ML environments (AWS/GCP/Azure preferred)

Preferred Skills :

- Experience implementing research papers into production-grade systems.

- Exposure to :

i. NLP Algorithms

ii. Computer Vision Models

iii. Generative AI concepts

- Familiarity with : Ethical AI Principles, Data Governance, Responsible AI practices

- Research publication or innovation-oriented background preferred.