Machine Learning Specialist

Role: Sr. AI ML Developer

Required Technical Skill Set: AI ML, Edge AI

Desired Experience Range: 8+ yrs

Location of Requirement: Hyderabad

Desired Competencies (Technical/Behavioral Competency)

Must-Have

1. 5+ years of hands-on development experience in AI/ML.
2. Strong knowledge of ML libraries (TensorFlow Lite, PyTorch Mobile, ONNX).
3. Experience with edge hardware platforms (e.g., Raspberry Pi, Jetson Nano, Coral Dev Board).
4. Proficient in Python and C/C++.
5. Familiarity with performance optimization techniques for models on edge.
6. Experience with REST APIs, messaging protocols, or low-latency data streaming.
7. Ability to perform predictive and statistical analysis from different data source
8. knowledge and hands-on experience of building and deploying AI models on edge devices.
9. knowledge of embedded systems, microcontrollers, or low-power compute devices.
10. Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines
11. Experience with Image Processing, Computer Vision, NLP, Pattern Recognition, Machine Learning and Linear algebra.
12. knowledge and exposure to model optimization techniques.
13. Experience with AI accelerator frameworks

Good-to-Have

1. Familiarity with OpenCV, YOLO, or MobileNet for vision tasks.
2. Knowledge of TinyML or microcontroller-based AI inference.
3. Exposure to MLOps tools and versioning (MLflow, DVC).
4. Understanding of security practices in edge deployments.
5. Experience with edge analytics, anomaly detection, or predictive maintenance use cases.
6. Exposure to deployment tool-chain like Intel EII, Nvidia Deep Stream, Qualcomm AI Hub, etc....
7. Excellent communication and documentation skills
8. Exposure to popular platforms such as Azure, AWS.

Responsibility of / Expectations from the Role

1. Build and optimize AI/ML models for edge deployment.
2. Develop edge inference pipelines using lightweight frameworks.
3. Optimize models for resource-constrained environments (quantization, pruning).
4. Integrate AI models into embedded or IoT platforms.
5. Collaborate with cross-functional teams on data collection, preprocessing, and annotation.
6. Implement software for real-time processing and decision-making at the edge.