ML Engineer

Location: Bangalore / Hybrid

Experience Level: Mid to Senior

About the Role

We are looking for an experienced Machine Learning Engineer to build and scale advanced forecasting and optimization systems. In this role, you will design, develop, and deploy high-performance ML solutions that operate on large-scale, real-time datasets to support intelligent, data-driven decision-making.

You will work closely with cross-functional teams to translate complex problem statements into robust, production-ready machine learning systems. This is a hands-on role with strong ownership across experimentation, system design, and deployment.

Key Responsibilities

• Design and implement end-to-end machine learning pipelines for large-scale, real-time use cases
• Build and enhance optimization engines using advanced ML and statistical techniques
• Train, fine-tune, and evaluate forecasting models on high-volume datasets
• Translate hypotheses and problem statements into deployable ML solutions through experimentation and validation
• Collaborate with engineering and research teams to integrate models into production systems
• Maintain clear documentation for code, models, and experimental results

What You’ll Gain

• Opportunity to work on complex, high-impact ML problems with measurable outcomes
• Hands-on experience building scalable and low-latency ML systems
• Exposure to modern ML frameworks, tools, and optimization techniques
• A collaborative environment that values ownership, experimentation, and continuous learning
• A role where your work directly influences system performance and business outcomes

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Data Science, or a related field (PhD is a plus)
• 3+ years of experience building and deploying machine learning models in production
• Strong proficiency in Python and ML libraries such as PyTorch, TensorFlow, scikit-learn, NumPy, and Pandas
• Solid understanding of supervised and unsupervised learning, time-series modeling, and statistical methods
• Experience with model evaluation, experimentation, and performance tuning
• Familiarity with Git, Docker, and Kubernetes is a plus
• Exposure to reinforcement learning or optimization techniques is desirable