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At NeuRelic Labs, we don’t just build models — we build real-time intelligence systems that move with the markets. As a Machine Learning Engineer, you’ll bridge the gap between cutting-edge AI research and scalable production pipelines. From transforming raw trading data into actionable features to deploying inference-ready models for our flagship platforms, you’ll play a crucial role in delivering precision, speed, and automation to the financial edge.
You’ll collaborate closely with researchers, backend engineers, and product designers to architect, deploy, and monitor ML models across our growing stack.
Build and maintain robust ML pipelines: data preprocessing, model training, evaluation, and deployment
Work with large-scale datasets from brokers, markets, and customer interactions to extract meaningful patterns
Translate ML research (e.g., from our AI Research team) into production-ready code using frameworks like PyTorch, TensorFlow, or scikit-learn
Optimize model latency, accuracy, and resource efficiency for real-time use cases in trading and analytics
Collaborate with product teams to align technical implementation with business goals and compliance guidelines
Monitor and retrain models based on drift, usage patterns, and updated datasets
Participate in model review, documentation, and continuous improvement cycles
Bachelor's/Master’s degree in Computer Science, Data Science, or related field
2–5 years of experience in building and deploying machine learning models in production
Strong Python skills; familiarity with ML Ops tools (MLflow, Weights & Biases, Airflow, etc.)
Experience with time series forecasting, anomaly detection, or NLP preferred
Exposure to trading data, fintech products, or real-time analytics is a plus
Comfort with version control, containerization, and cloud-native workflows (AWS/GCP)
Provide expert guidance on developing an AI strategy
Remote / Hybrid
10 - 12 Lakhs INR
Conducting cutting-edge research in AI, developing new algorithms, and pushing the boundaries of AI capabilities.
Designing and implementing machine learning models and systems, and optimizing algorithms for real-world applications.
Overseeing the development and deployment of AI-powered products and solutions, and working closely with cross-functio