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


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


  • Job Details

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Unlock Your 3 Days Potential: Insight & Career Growth Guide


Real-time 3 Days Jobs Trends (Graphical Representation)

Explore profound insights with Expertini's real-time, in-depth analysis, showcased through the graph here. Uncover the dynamic job market trends for 3 Days in Bengaluru, India, highlighting market share and opportunities for professionals in 3 Days roles.

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Are You Looking for 3 Days Left: Machine Learning Engineer Job?

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The Work Culture

An organization's rules and standards set how people should be treated in the office and how different situations should be handled. The work culture at ANSR adheres to the cultural norms as outlined by Expertini.

The fundamental ethical values are:

1. Independence

2. Loyalty

3. Impartiapty

4. Integrity

5. Accountabipty

6. Respect for human rights

7. Obeying India laws and regulations

What Is the Average Salary Range for 3 Days Left: Machine Learning Engineer Positions?

The average salary range for a varies, but the pay scale is rated "Standard" in Bengaluru. Salary levels may vary depending on your industry, experience, and skills. It's essential to research and negotiate effectively. We advise reading the full job specification before proceeding with the application to understand the salary package.

What Are the Key Qualifications for 3 Days Left: Machine Learning Engineer?

Key qualifications for 3 Days Left: Machine Learning Engineer typically include Computer Occupations and a list of qualifications and expertise as mentioned in the job specification. The generic skills are mostly outlined by the . Be sure to check the specific job listing for detailed requirements and qualifications.

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Interview Tips for 3 Days Left: Machine Learning Engineer Job Success

ANSR interview tips for 3 Days Left: Machine Learning Engineer

Here are some tips to help you prepare for and ace your 3 Days Left: Machine Learning Engineer job interview:

Before the Interview:

Research: Learn about the ANSR's mission, values, products, and the specific job requirements and get further information about

Other Openings

Practice: Prepare answers to common interview questions and rehearse using the STAR method (Situation, Task, Action, Result) to showcase your skills and experiences.

Dress Professionally: Choose attire appropriate for the company culture.

Prepare Questions: Show your interest by having thoughtful questions for the interviewer.

Plan Your Commute: Allow ample time to arrive on time and avoid feeling rushed.

During the Interview:

Be Punctual: Arrive on time to demonstrate professionalism and respect.

Make a Great First Impression: Greet the interviewer with a handshake, smile, and eye contact.

Confidence and Enthusiasm: Project a positive attitude and show your genuine interest in the opportunity.

Answer Thoughtfully: Listen carefully, take a moment to formulate clear and concise responses. Highlight relevant skills and experiences using the STAR method.

Ask Prepared Questions: Demonstrate curiosity and engagement with the role and company.

Follow Up: Send a thank-you email to the interviewer within 24 hours.

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Be Yourself: Let your personality shine through while maintaining professionalism.

Be Honest: Don't exaggerate your skills or experience.

Be Positive: Focus on your strengths and accomplishments.

Body Language: Maintain good posture, avoid fidgeting, and make eye contact.

Turn Off Phone: Avoid distractions during the interview.

Final Thought:

To prepare for your 3 Days Left: Machine Learning Engineer interview at ANSR, research the company, understand the job requirements, and practice common interview questions.

Highlight your leadership skills, achievements, and strategic thinking abilities. Be prepared to discuss your experience with HR, including your approach to meeting targets as a team player. Additionally, review the ANSR's products or services and be prepared to discuss how you can contribute to their success.

By following these tips, you can increase your chances of making a positive impression and landing the job!

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