AI/ML Engineer

Reliance Intelligence; Navi Mumbai; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Reliance Intelligence is seeking an engineer to develop and optimize AI solutions for multilingual assistants in consumer electronics and IoT. You will design, train, and fine-tune ML models, with a focus on real-time inference and robust deployment across cloud environments.

The role emphasizes collaboration with data engineers and scientists, feature engineering, and scalable AI systems that serve millions of users. Proficiency in Python and ML frameworks is required.

Qualifications

• 2–4 years of experience at engineer level; 6–12 years for senior level.
• Experience in Python and ML frameworks (PyTorch or TensorFlow).
• Experience deploying ML models into production systems.
• Hands-on with cloud platforms (AWS, GCP, or Azure).
• Familiarity with streaming or real-time ML pipelines.

Responsibilities

• Design, train, and fine-tune ML and DL models for various use cases.
• Build low-latency real-time inference pipelines and integrate with streaming data systems.
• Deploy models using Docker, Kubernetes, and cloud-native ML services; implement CI/CD for training and deployment.
• Collaborate with Data Engineers and Data Scientists to design feature pipelines and stores.
• Optimize models for scalable inference and monitor drift with retraining strategies.

Skills

Python

PyTorch

TensorFlow

Production deployments

Cloud platforms

Streaming pipelines

Tools

Docker

Kubernetes

Job description

This role builds and improves the voice, vision and language intelligence behind a multilingual AI assistant that runs on smart wearables and connected home devices, within the consumer electronics, AI and IoT space.

Engineers work mainly in Python, developing speech recognition, text-to-speech, language and vision pipelines, integrating LLM APIs, and writing and refining prompts while managing conversation context and memory so the assistant responds accurately and naturally.

Responsibilities

Model Development and Training.

Design, train, and fine-tune ML and deep learning models for recommendation, personalization,

computer vision, speech, and reinforcement learning use cases. Experiment with architectures, loss

functions, and evaluation metrics to improve model accuracy, latency, and robustness.

Real-Time Inference & Decision Systems

Build low-latency inference pipelines for real-time predictions and feedback loops. Integrate models

with streaming data systems to enable online learning and adaptive decision-making.

Deploy models using Docker, Kubernetes, and cloud-native ML services. Implement CI/CD

pipelines for model training, testing, and deployment. Manage model versioning, monitoring, drift

detection, and retraining strategies.

Feature Engineering & Data Collaboration

Partner with Data Engineers and Data Scientists to design feature pipelines and feature stores.

Ensure reproducible experiments and scalable feature access across teams.

Optimization & Scalability

Optimize models for inference at scale using quantization, pruning, and hardware-aware

optimizations. Ensure high throughput and reliability for AI systems serving millions of users.

Qualifications

Typical experience is 2 to 4 years for the engineer level and 6-12 years for the senior level.

Required Skills

• Strong experience in Python and ML frameworks such as PyTorch or TensorFlow.
• Experience deploying ML models into production systems.
• Solid understanding of ML system design, inference optimization, and scalability.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure).
• Familiarity with streaming or real-time ML pipelines.
• Strong experience in Python and ML frameworks such as PyTorch or TensorFlow.
• Experience deploying ML models into production systems.
• Solid understanding of ML system design, inference optimization, and scalability.
• Hands-on experience with cloud platforms (AWS, GCP, or Azure).
• Familiarity with streaming or real-time ML pipelines.