Edge AI CTO for Video Analytics

Navigate Linux filesystems, permissions, device files, and mounted storage from the command line.

Inspect and control Linux processes, services, logs, and resource usage.

Write a shell script that automates a repeatable development or deployment task.

Implement and refactor a typed Python component using core data structures, functions, classes, and exception handling.

Structure a Python application with isolated dependencies, configuration, logging, and clear package boundaries.

Use a Git branch-and-review workflow to make, inspect, merge, and revert changes safely.

Use unit and interface tests, stack traces, structured logs, and a minimal reproduction to diagnose a failing Python component.

Validate AI-generated code with a review checklist, static checks, tests, and dependency inspection.

Map the team's competencies against product needs and choose whether to train, hire, or contract for each critical gap.

Assign clear technical ownership and decision rights using a lightweight responsibility model.

Establish a definition of done with coding, testing, documentation, security, and release requirements.

Lead a design review that exposes assumptions, failure modes, trade-offs, and unresolved evidence.

Decompose a milestone into bounded tasks and delegate them using checkpoints and progressively increasing responsibility.

Run a structured one-to-one that gives specific feedback, sets expectations, and records follow-up actions.

Conduct a structured technical interview using job-relevant evidence and a consistent scoring rubric.

Lead a blameless post-incident review that produces owned and verifiable corrective actions.

Trace how CPU execution, memory allocation, storage I/O, and data copies affect application performance.

Compile and debug a small C++ application with CMake and external libraries.

Manage C++ resources safely using object lifetimes, RAII, and smart pointers.

Implement a bounded producer-consumer pipeline that handles concurrency and backpressure.

Select among TCP, UDP, HTTP, RTSP, and MQTT for a stated edge-system communication need.

Diagnose device connectivity using addresses, routes, DNS, ports, sockets, and packet inspection.

Compare raw and encoded media formats, select an appropriate codec profile, and estimate bandwidth and storage requirements.

Design timestamping, buffering, reconnection, and clock-synchronization behavior for aligned audio and video streams.

Map the CPU, GPU, accelerators, media engines, shared memory, and I/O of Jetson Orin Nano and Jetson Thor, and identify workload and software-compatibility implications.

Install and verify a vendor-supported JetPack or Jetson Linux environment on a non-production target and document its recovery procedure.

Enumerate and validate camera and microphone capabilities using Linux media and audio interfaces.

Configure Jetson power and performance modes for a stated deployment constraint.

Measure CPU, GPU, memory, temperature, throttling, and power behavior during a workload.

Run a GPU-enabled, architecture-compatible container on Jetson using the NVIDIA container runtime.

Specify storage, power supply, cooling, enclosure, and environmental requirements for an edge device.

Create a reproducible benchmark that compares latency, throughput, memory use, temperature, and power across Jetson targets.

Represent images, video clips, and audio segments as tensors while tracking shape, data type, range, and memory layout.

Trace a batch through a simple neural network and explain the roles of labels, predictions, loss, gradients, optimization, and inference.

Compute confusion-matrix metrics and bootstrap uncertainty intervals, then choose a decision threshold from the costs of false positives and false negatives.

Create train, validation, and test splits that prevent identity, location, and temporal data leakage.

Diagnose underfitting and overfitting from learning curves and select an appropriate corrective action.

Design an annotation schema and a sampling-based quality-control process for an audio or video dataset.

Run and record a reproducible transfer-learning experiment in PyTorch.

Compare task-specific and foundation-model candidates using quality, latency, throughput, memory, power, licensing, and performance across environmental and fairness-relevant data slices.

Validate and transform camera inputs while accounting for resolution, color format, lens distortion, and downstream coordinate mappings.

Select among classification, detection, segmentation, pose estimation, tracking, temporal recognition, and vision-language analysis for a stated video requirement.

Fine-tune an object detector on a custom dataset using transfer learning.

Evaluate and tune an object detector using precision-recall behavior, mAP, confidence thresholds, and non-maximum suppression.

Configure multi-object tracking and evaluate identity consistency on representative clips.

Implement region-of-interest, line-crossing, dwell-time, and count logic from tracked objects.

Prototype one advanced vision approach—segmentation, pose, temporal recognition, or vision-language analysis—and measure whether its additional cost improves the target decision.

Construct a video robustness test matrix covering lighting, viewpoint, motion, occlusion, crowding, camera drift, and scene changes.

Relate sample rate, bit depth, channels, amplitude, frequency, and signal-to-noise ratio to an audio analytics requirement.

Inspect waveforms and spectrograms to identify speech, noise, clipping, silence, and recurring acoustic events.

Capture, resample, frame, normalize, and validate microphone audio for model input.

Apply voice-activity or acoustic-event detection to segment a continuous audio stream.

Train and evaluate a transfer-learned sound-event classifier on a custom dataset.

Run automatic speech recognition and calculate word error rate on domain- and language-representative speech.

Evaluate speaker diarization on overlapping and non-overlapping speech segments.

Design a far-field audio test matrix covering microphone placement, background noise, reverberation, languages, accents, code-switching, and privacy constraints.

Build a GStreamer pipeline that captures, decodes, transforms, and displays or stores an audio/video stream.

Diagnose GStreamer capability negotiation, queueing, latency, and dropped-buffer failures.

Explain how CUDA execution, memory transfers, batching, and synchronization influence inference performance.

Use system and NVIDIA profiling evidence to identify the dominant bottleneck in a media-AI pipeline.

Export a trained PyTorch model to ONNX and verify numerical agreement on representative inputs.

Build and benchmark an FP16 TensorRT engine with appropriate input shapes and memory settings.

Calibrate and evaluate an INT8 TensorRT engine while checking accuracy loss and unsupported operations.

Configure and validate a reference DeepStream multi-stream inference pipeline that emits application-ready metadata.

Partition inference, storage, control, and user-facing functions between device, local network, and cloud for a stated scenario.

Package a multi-service edge application with containers, health checks, persistent volumes, and explicit dependencies.

Create a continuous-integration workflow that tests and packages an edge application for its target architecture.

Version application, configuration, dataset, and model artifacts so a deployed result can be reproduced and rolled back.

Implement idempotent store-and-forward behavior that survives intermittent connectivity without duplicating or losing events.

Design a signed over-the-air update strategy with staged rollout, compatibility checks, and rollback.

Define fleet telemetry for device health, application health, model behavior, and resource consumption.

Create a model-monitoring and data-feedback process that detects drift and selects useful samples for review.

Define service-level indicators, service-level objectives, and recovery targets for an edge analytics deployment.

Design fault-injection tests for power loss, process crashes, storage exhaustion, network failure, overheating, and sensor loss.

Create a threat model for device, network, update, model, API, software-supply-chain, and media-data attack surfaces.

Apply a prioritized Jetson hardening baseline covering least privilege, exposed services, secrets, secure boot, storage protection, and physical debug access.

Configure PKI-backed device identity and mutual TLS authentication for a Jetson device.

Design access, encryption, retention, deletion, and audit controls for captured media and derived metadata.

Map an audio/video workflow to the then-current Indian Digital Personal Data Protection framework, explicitly marking assumptions and questions requiring qualified legal counsel.

Write an incident-response runbook covering containment, evidence, recovery, client communication, and corrective action.

Convert a customer workflow into a product hypothesis with a target user, measurable value, and falsifiable assumptions.

Prioritize and sequence a product and platform roadmap using customer value, technical risk, effort, strategic reuse, dependencies, and urgency.

Estimate per-deployment and recurring costs, price floors, support burden, and gross-margin scenarios for an edge-AI offering.

Construct an engineering budget covering people, hardware, software, data, infrastructure, contingency, and runway.

Structure a pilot statement of work with acceptance criteria, responsibilities, exclusions, change control, and production boundaries.

Perform strategic due diligence on a vendor, model, dataset, or open-source dependency using licensing, intellectual property, support, lock-in, and exit criteria.

Present architecture, evidence, uncertainty, cost, and risk trade-offs to a non-technical decision-maker.

Produce a 90-day CTO operating plan that links architecture, pilot delivery, team development, security, finances, and measurable company outcomes.