Senior Machine Learning Engineer

Location: Bengaluru

Job Overview:

We are seeking an experienced Senior Machine Learning Engineer (Computer Vision) with 8–10 years of experience in building, deploying, and optimizing ML and computer vision solutions  role focuses on developing end-to-end ML systems grounded in strong data science fundamentals, while increasingly integrating Gen AI capabilities such as RAG, multimodal models, and agent-based workflows.
The candidate will work closely with product, platform, and MLOps teams to design reliable, scalable ML components that move from experimentation to production, ensuring performance, accuracy, and long-term maintainability.

Key Responsibilities:

Design, develop, and deploy computer vision and machine learning models for real-world business use cases.
Apply strong data analysis, statistical reasoning, and feature engineering to improve model performance and robustness.
Build and optimize deep learning models using CNNs and Transformer-based architectures for vision and multimodal tasks.
Own the end-to-end ML lifecycle, including data preparation, training, evaluation, tuning, and production deployment.
Collaborate with MLOps teams to support model versioning, monitoring, retraining, and performance tracking in production.
Design ML components that integrate seamlessly into larger application and platform architectures.
Contribute to GenAI initiatives, including building RAG pipelines that combine vision and text data, and integrating CV outputs into LLM workflows.
Implement and evaluate single-agent and limited multi-agent workflows where applicable.
Select appropriate evaluation metrics and clearly explain model trade-offs, limitations, and outcomes to stakeholders.
Mentor junior engineers and contribute to knowledge sharing and best practices within the team.
Collaborate closely with cross-functional teams including product, data, platform, and engineering to deliver end-to-end solutions.

Key Skills and

Qualifications:

Strong foundation in machine learning, deep learning, and data science principles, including supervised and unsupervised learning.
Hands-on experience with computer vision models, including CNNs and Transformer-based architectures.
Proficiency in Python and common ML/data libraries, with solid understanding of data structures and algorithms.

Experience with SQL, data pre-processing, and feature engineering for structured and unstructured datasets.
Practical experience deploying ML models into production environments and handling challenges such as data imbalance, drift, and noisy labels.
Familiarity with cloud-based ML workflows on platforms such as AWS, Azure.
Exposure to distributed training or large-scale data processing environments.
Working knowledge of MLOps practices, including monitoring, retraining, and experiment tracking.

Experience with GenAI concepts, including RAG systems, multimodal pipelines, lightweight fine-tuning, and evaluation of LLM outputs.
Strong communication skills with the ability to explain complex technical concepts to both technical and non-technical stakeholders.
Experience in Edge based deployment on AI models ex CoreML for iOS, and Tensor Flow Lite (TFLite) for mobile deployment.
Knowledge about Hardware Acceleration

Experience with NVIDIA Jetson, Coral Edge TPU, or mobile NPUs (Neural Processing Units).
Video Orchestration: GStreamer or FFmpeg for low-latency camera stream ingestion.
Frameworks:
PyTorch or Tensor Flow;
Torch Vision for vision-specific primitives.
Inference Optimization using NVIDIA Tensor

RT, OpenVINO, or TFLite for low-latency performance.
Experience in sports domain will be a plus.

Educational Background:

Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
Advanced certifications such as  AWS Certified Machine Learning – Specialty ,Microsoft Certified:
Azure AI Engineer Associate are good to have.
Specialized training in Machine Learning, AI, or Cloud ML platforms are a plus.