IoT/ML Internship | Spypro Hack You

Program Overview

Real Sensors. Real Edge.
Real Devices.

This isn't a theoretical IoT survey. From day one you'll be reading live sensor streams, running TensorFlow Lite inference on microcontrollers, securing MQTT brokers, and feeding telemetry into cloud dashboards alongside experienced IoT engineers.

We built this program around what employers actually need: solid signal-processing discipline, embedded ML intuition, connectivity protocol knowledge, and the ability to ship reliable edge intelligence into real devices.

4-6 months

Remote & hybrid

Certificate

Part-time ok

Live device projects

edge_inference.py spypro-iot

            import tflite_runtime.interpreter as tflite
            import paho.mqtt.client as mqtt
            import numpy as np
            

            # Load quantised model onto edge device
            interpreter = tflite.Interpreter(
              model_path="anomaly_detect_int8.tflite"
            
            interpreter.allocate_tensors()
            

            def on_sensor_msg(client, userdata, msg):
              reading = np.frombuffer(msg.payload, dtype=np.float32)
              score = run_inference(interpreter, reading)
              if score > 0.92: client.publish("alerts/anomaly", score)
            

            Subscribed to sensors/vibration ?
             Inference latency: 4.2 ms
             Alert published to cloud dashboard
            

            $ python monitor_fleet.py 
          

Download Curriculum

Choose your preferred internship duration and download the detailed curriculum to plan your learning journey

What You'll Learn

Six Core Skill Domains

A curriculum shaped by practising IoT engineers and ML researchers building connected intelligence at product companies and labs.

📡

Sensor Data Acquisition & Processing

Interface with temperature, vibration, accelerometer, and environmental sensors. Apply filtering, normalisation, and feature extraction pipelines to prepare raw streams for ML consumption.

NumPySciPyPandas

🧩

Embedded ML & Optimisation

Train classification and anomaly-detection models and compress them for edge deployment using quantisation and pruning. Run TensorFlow Lite and ONNX inference on Raspberry Pi and microcontrollers.

TFLiteONNXEdge Impulse

🔗

MQTT / CoAP Connectivity

Design secure, low-latency device-to-cloud messaging pipelines using MQTT and CoAP protocols. Implement TLS authentication, topic hierarchies, QoS levels, and broker failover strategies.

MosquittoPahoCoAP

☁️

Cloud Ingestion & Monitoring

Stream telemetry into AWS IoT Core or Google Cloud IoT, store time-series data in InfluxDB, and build live dashboards in Grafana. Set up alerting and anomaly thresholds for fleet health monitoring.

AWS IoTInfluxDBGrafana

🔒

Device Security & OTA Updates

Implement certificate-based device authentication, encrypted firmware storage, and secure over-the-air update pipelines. Study threat modelling for resource-constrained hardware environments.

TLS/mTLSOTARBAC

🚀

Production Edge Deployment

Package edge models as containerised services with Docker, orchestrate multi-device fleets, and build a Streamlit dashboard for real-time device analytics with end-to-end observability.

DockerStreamlitPrometheus

Program Timeline

Your Journey, Month by Month

A structured ramp from IoT fundamentals to shipping production-ready edge-intelligent connected applications.

MONTH 1

IoT Foundations & Sensor Interfacing

Hardware overview, GPIO programming, and sensor interface protocols (I?C, SPI, UART). Collect live data from temperature, humidity, and accelerometer sensors. Write Python pipelines to clean, normalise, and visualise raw streams. Mentorship kick-off with your assigned IoT engineer.

MONTH 2

Connectivity Protocols & Cloud Ingestion

Deploy a secured Mosquitto MQTT broker, implement TLS device authentication, and design topic hierarchies for a multi-sensor fleet. Stream live telemetry into AWS IoT Core, store it in InfluxDB, and build your first Grafana monitoring dashboard.

MONTH 3

Embedded ML & Edge Optimisation

Train anomaly-detection and classification models on your sensor datasets. Apply int8 quantisation and pruning to meet edge constraints. Deploy TFLite and ONNX models on Raspberry Pi, measure latency, power draw, and accuracy trade-offs in real hardware conditions.

MONTH 4?6 / GRADUATION

Security, OTA & Capstone Deployment

Implement secure OTA firmware update pipelines and certificate-based device identity. Package your edge ML system as a containerised service with full-fleet orchestration. Present your capstone connected-device application to industry guests and receive your verified certificate, LinkedIn endorsement, and referrals to IoT-first hiring partners.

Tech Stack

Tools You'll Master

Python 3.12

TensorFlow Lite

ONNX Runtime

Edge Impulse

Paho MQTT

Mosquitto Broker

AWS IoT Core

InfluxDB

Grafana

Docker

Raspberry Pi / Arduino

Streamlit

Prometheus

mTLS / OTA

Eligibility

Who Should Apply?

We value Python fluency and hardware curiosity over existing IoT credentials ? the field moves fast and we teach what matters now.

Ideal Candidates

CS, IT, EEE, or electronics engineering students (bachelor/master)
Solid Python - functions, classes, serial/socket I/O
Basic networking fundamentals - TCP/IP, ports, protocols
Curious about hardware - tinkered with Arduino, Pi, or sensors
Completed at least one Python or ML project or course
Excited by the idea of AI running directly on devices

Common Barriers (We Help With)

No prior IoT or embedded systems experience required
No deep learning or hardware background needed upfront
No research publications or certifications mandatory
Non-hardware backgrounds (data science, web dev) welcome
Part-time track available for working students

Application

Start Your Application

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Full Name

Email Address

Phone Number

Current Qualification

Preferred Track

Select Internship Domain

Start Availability

Why are you right for this internship?

Your information is encrypted and never shared with third parties.

FAQ

Common Questions

Is this internship paid?

Stipends for outstanding performers from month 2. All interns receive a verified certificate, LinkedIn endorsement, and placement support at IoT startups, hardware labs, and industrial tech companies.

Can I do this while studying full-time?

Yes ? our part-time track requires around 20 hrs/week, is structured around academic schedules, and includes flexible lab windows and recorded sessions for async work.

What hardware do I need?

A modern laptop (8 GB+ RAM) and stable internet. We ship an IoT starter kit (Raspberry Pi + sensors) or you can use our cloud-based device simulators ? no expensive lab setup required.

How competitive is selection?

We accept roughly 20% of applicants per cohort, prioritising Python fluency, networking fundamentals, and genuine curiosity about hardware and edge computing.

Will I work with real devices?

Yes ? interns work with actual Raspberry Pi hardware, real sensor suites, live MQTT brokers, and cloud IoT platforms, shipping real edge ML applications on physical devices.

What career paths does this open?

IoT engineer, embedded ML engineer, firmware developer, edge computing specialist, IoT solutions architect, and industrial AI engineer roles at hardware companies, manufacturing firms, and smart infrastructure startups.

Sense. Process. Infer.IoT / ML Internship

Real Sensors. Real Edge.Real Devices.