AI/ML Practice Lead

Job Function, Roles & Responsibilities:

Lead strategic initiatives and own the practice for Edge AI/ML , data pipelines, and intelligent embedded systems

Define and build the competency roadmap for machine learning, deep learning, model deployment, and real-time inferencing on edge platforms

Oversee data creation including data collection, dataset curation, annotation, cleaning, augmentation, and synthetic data generation

Champion use cases involving sensor fusion, combining data from multiple sources (vision, IMU, radar, audio, etc.) to create robust, efficient, and context-aware edge intelligence solutions

Drive edge analytics and on-device learning across verticals such as Industrial Automation, Medical Devices, Automotive, and Smart Consumer Electronics

Collaborate with global customers to gather requirements, architect solutions, track project delivery, and ensure alignment with business objectives

Support business development with presales solutioning, proposal writing, and effort estimation

Drive internal capability building through mentoring, training, and competency development

Experience:

10+ years in embedded systems, AI/ML, and data engineering, with a strong focus on edge intelligence and real-time systems. At least 3 years in a technical leadership or strategic role. Prior experience in a product engineering services environment preferred.

Area of Expertise:

Proven expertise in deploying ML/DL models on edge devices (NVIDIA Jetson, NXP i.MX, Qualcomm QCS, TI Sitara, etc.)

Strong knowledge of data workflows: dataset generation, manual/automated annotation, data cleaning, augmentation, and synthetic data creation

Deep understanding of sensor fusion techniques combining inputs from vision, audio, IMU, radar, LIDAR, and other sources to improve model accuracy and efficiency

Experience in model optimization using TensorRT , ONNX, OpenVINO, TFLite, and TVM

Hands-on with TensorFlow , PyTorch, scikit-learn, and signal/image processing techniques

Proficient in designing for real-time inference on resource-constrained platforms

Exposure to AI accelerators, NPUs, DSPs, and hybrid SoC environments; must have exposure to NVIDIA SoC & Tools

Presales, account engagement, and solutioning experience with North American or European clients

Nice to Have:

Cloud-edge integration using AWS Greengrass, Azure IoT Edge, GCP Edge TPU

Understanding of AI regulatory/safety standards (ISO, IEC, FDA compliance for AI/ML in regulated industries)

Educational Criteria:

BE/ME/B.Tech/M.Tech Electronics, Computer Science, AI/ML, Embedded Systems, or Data Science

Travel:

Flexibility to travel globally with sales or delivery teams for customer meetings, workshops, and project deployments as needed.

Checklist:

1. Has the candidate worked with multi-sensor data pipelines and sensor fusion techniques?

2. Does the candidate have experience with data creation, annotation, cleaning, and synthetic augmentation?

3. Has the candidate deployed ML models on edge devices like Jetson, QCS, or i.MX?

4. Is the candidate experienced in solutioning and presales for global customers?

5. Has the candidate worked with North American or European clients in the last 3 years?

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