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Urgent! 3 Days Left: Machine Learning Engineer Job Opening In Bengaluru – Now Hiring ANSR

3 Days Left: Machine Learning Engineer



Job description

As a Quantitative Machine Learning Engineer at Merli, you will help shape the next generation of AI-driven trading infrastructure.

This role sits at the intersection of quantitative research, applied ML, and agentic system design, with the goal of optimizing high-frequency (HFT), medium-frequency (MFT), and wholesale trading strategies.

You’ll architect adaptive, agent-based ML systems that learn from evolving market microstructures, build robust forecasting and optimization models, and work with trading and infrastructure teams to deploy these solutions in real-world, low-latency environments.

Key Responsibilities Design & Build Agentic ML Systems: Develop autonomous and semi-autonomous agents that perform data acquisition, alpha discovery, backtesting, and execution optimization.

End-to-End ML Engineering: Architect, train, and deploy ML pipelines for HFT/MFT and wholesale trading, from signal generation to execution integration.

Quantitative Research Integration: Collaborate with quant researchers to translate theoretical models into production-ready predictive and optimization systems.

Market Forecasting: Develop deep learning, time series, and reinforcement learning models for price movement prediction and regime detection.

Trading Optimization: Build reinforcement and meta-learning frameworks that adaptively tune strategy parameters in live environments.

Scalable ML Infrastructure: Implement real-time inference, model versioning, and continuous learning pipelines for production systems.

Performance Evaluation: Rigorously validate models with historical and synthetic simulations, ensuring robustness, latency, and financial soundness.

Documentation & Collaboration: Maintain high standards of reproducibility, version control, and code documentation across research and deployment layers.

What You’ll Gain Work at the frontier of AI, quantitative finance, and agentic automation.

Collaborate with quant researchers, data engineers, and trading teams shaping next-gen trading systems.

Exposure to meta-optimization frameworks, reinforcement learning, and multi-agent orchestration.

Hands-on experience with low-latency ML deployment, GPU acceleration, and distributed training in real trading environments.

Ownership of models that directly influence market-making, forecasting, and strategy execution.

Continuous learning and experimentation in a research-first, innovation-driven environment.

Qualifications Bachelor’s, Master’s, or Ph.D. in Computer Science, Applied Mathematics, Financial Engineering, or a related quantitative field.

3+ years of experience developing and deploying ML models in production (preferably in finance, trading, or large-scale decision systems).

Strong proficiency in Python and ML frameworks: PyTorch, TensorFlow, scikit-learn, NumPy, Pandas.

Deep understanding of supervised, unsupervised, and reinforcement learning, time-series modeling, and probabilistic forecasting.

Experience building scalable data pipelines with Kafka, Flink, or Ray and deploying models in Docker/Kubernetes environments.

Knowledge of market microstructure, portfolio optimization, or signal-based trading systems is highly desirable.

Familiarity with meta-learning, agent-based system design, or multi-agent coordination is a strong plus.

Solid analytical, programming, and debugging skills with an emphasis on system reliability and latency optimization.

Preferred Technical Stack Languages: Python, C++, Rust ML Infrastructure: Ray, MLflow, Airflow, Weights & Biases Data Systems: Kafka, Redpanda, Redis, QuestDB Model Deployment: Triton Inference Server, TorchServe, or custom GPU inference Cloud/Hybrid Setup: Kubernetes, ArgoCD, Helm


Required Skill Profession

Computer Occupations



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