AI Engineer – Machine Control

1 week ago

Greater Bengaluru Area, India Tata Electronics Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Role Overview

We are looking for a
Machine Control Engineer
with strong
coding skills
and a
passion for learning AI, reinforcement learning (RL), and intelligent automation
. This role will focus on
developing and optimizing control systems for complex industrial machines
, integrating AI-driven approaches to improve precision, efficiency, and automation.

Key Responsibilities


• Develop, optimize, and maintain
real-time machine control systems
for
precision manufacturing and automation
.


• Implement
algorithm-driven control logic
using
Python, C++, or MATLAB
.


• Work with
PLC, motion control, and CNC systems
for high-precision equipment.


• Integrate
AI/ML-based models
into machine control workflows for
predictive maintenance, anomaly detection, and process optimization
.


• Explore and apply
reinforcement learning (RL) techniques
to improve machine decision-making and adaptive control.


• Design
simulation environments
for machine behavior testing and optimization.


• Collaborate with
AI, automation, and manufacturing teams
to develop intelligent control solutions.


• Troubleshoot, debug, and optimize real-time control systems for
efficiency, accuracy, and reliability
.

Qualifications & Experience

Bachelor's or Master's degree in
Electrical Engineering, Mechanical Engineering, Computer Science, Mechatronics, or a related field (
CE/EE/CS/IE/Mfg).

1 to 3 years
of experience in machine control, automation, or industrial systems.

Proficiency in
Python, C++, or MATLAB
for control system development.

Experience with
PLC programming, motion control, or robotic automation
.

Knowledge of
real-time computing, embedded systems, or hardware-in-the-loop (HIL) testing
.

Willingness to learn
AI, Machine Learning, and Reinforcement Learning
for next-gen machine control.

Strong debugging, problem-solving, and analytical skills.

Bonus Skills

Experience with
digital twins, IoT, or edge computing
in industrial applications.

Knowledge of
feedback control theory, optimal control, or PID tuning
.

Exposure to
deep reinforcement learning (DQN, PPO, SAC) or model-based control
.

Familiarity with
robotic simulation tools (MuJoCo, PyBullet, or Gazebo)
.

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