Machine Learning Engineer Dl/rl Chengalpattu (India)

Job Summary

We are looking for a person with a deep understanding of Machine Learning and Reinforcement learning to develop tools that help us an organisation engineer better systems and also build the platform software that will allow the next generation of mobility to thrive. This person will develop, research, and deploy ML algorithms across different engineering disciplines and build tools for routing, scheduling future mobility transportation networks involving hundreds of aircraft in shared regions.

Roles and Responsibilities

Build and maintain data pipelines for model training, validation, and continuous retraining

Develop training pipelines, architecture, and prototyping for ML/RL algorithms

Work on productising research prototypes

Conduct experiments to benchmark new techniques and evaluate model behavior

Develop systematic evaluation methodology: test sets, accuracy metrics, citation quality scoring, false positive/negative analysis

Deploy AI tools to engineering teams with structured pilots, baseline measurement, and documented adoption outcomes

Build and operate multi-step agentic workflows connecting engineering data sources for reasoning

Requirements

3+ years ML engineering with a focus on deep learning/reinforcement learning

Robust ML stack: PyTorch or TensorFlow, Pandas, NumPy, SciPy

Solid programming skills in Python/C++

Hands on experience implementing Neural network architectures like CNNs, Transformers, RNNs, VAEs, Optimization of DL models(Memory, execution time, size) for inference

Working knowledge of full life cycle and various SDLC methodologies to meet project goals

Ability to design production ML systems that fail gracefully and whose failure modes are understood and documented

Reinforcement learning experience: policy training, reward engineering, simulation setting construction

Preferred Qualifications

Gradient-based optimization

Automatic differentiation tools and development

Experience developing ML systems in Safety-critical or regulated domain background where AI output quality must be explainable

Experience building multi-step agentic workflows

Familiarity with aerospace change management processes

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