About the Role

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.

MLOps & Productionization

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.
- Solid 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.

📌 AI/ML Engineer (Navi Mumbai)
🏢 Reliance Intelligence
📍 Navi Mumbai