Lead Developer & Architect – IP Camera

Thinkpalm Technologies Pvt.Ltd

Posted 8 Oct 2026

On-site
Kochi

Job description

JOBWAVE VERIFIED

Lead Developer & Architect – IP Camera

Company: ThinkPalm Technologies Pvt. Ltd. Location: Not specified Experience: 8+ Years Education: Not specified Work Mode: Not specified Employment Type: Not specified Notice Period: Immediate to 30 Days Core Domain: IP Cameras / Embedded Video / Real-Time Streaming Primary Languages: C / C++ OS / Platform: Embedded Linux Video: H.264, H.265, RTSP/RTP, WebRTC Networking: TCP/UDP, P2P, NAT Traversal, ICE, STUN, TURN Camera Technologies: Sensors, ISP, Hardware Encoders, SoC Multimedia Architecture: High-Performance / Real-Time Embedded Systems AI: Video Event Detection & Analytics Preferred Technologies: GStreamer, FFmpeg, V4L2, libcamera, ONVIF, MQTT, Embedded AI/NPUs Salary: Not specified

────────────────────

JOBWAVE INSIGHTS

Who is this for?

This is a highly specialized senior technical leadership role for an engineer with deep experience in C/C++, embedded Linux, IP cameras, real-time video processing, networking, and low-latency streaming.

This is not a general software architect role. The strongest candidates will have hands-on experience building camera or video-streaming systems, ideally involving P2P connectivity and remote camera access.

The role combines architecture + hands-on development + technical leadership.

Key Skills

C/C++
Embedded Linux
IP camera architecture
Real-time video pipelines
Video encoding/decoding
H.264 / H.265
RTSP / RTP
WebRTC
Low-latency streaming
UDP / TCP
P2P connectivity
NAT traversal
ICE / STUN / TURN
Multithreading
Hardware video encoders
Camera sensors / ISP
SoC multimedia
System architecture
Performance optimization
Debugging
Technical leadership

Priority Skills

Highest Priority

Strong C/C++ hands-on development
Deep embedded Linux experience
Real-time video streaming
H.264/H.265 encoding and decoding
Ultra-low-latency optimization
P2P / NAT traversal
ICE, STUN, TURN
IP camera architecture
Multithreaded embedded systems
System architecture & technical leadership

Strong Advantages

GStreamer
FFmpeg
V4L2
libcamera
WebRTC
RTSP/RTP
ONVIF
Camera SoCs
Embedded AI / NPUs
Object detection
MQTT
Cloud-connected cameras

What to Prepare

Advanced C/C++
Embedded Linux
Linux debugging
Multithreading and synchronization
Video codecs
H.264/H.265
GOP and bitrate control
Buffering and latency
RTP/RTSP
WebRTC
UDP/TCP
NAT traversal
ICE/STUN/TURN
P2P architecture
Camera sensors and ISP
Hardware encoders
SoC multimedia pipelines
GStreamer/FFmpeg
System architecture
Real-time performance optimization

What to Focus On

1. Real-Time Video Streaming

This is likely the core technical area of the role. Be able to explain the complete video path:

Camera Sensor → ISP → Capture → Encoding → Packetization → Network → Decoding → Playback

Understand where latency can be introduced and how to minimize it.

2. Low-Latency Optimization

Prepare practical techniques for reducing end-to-end latency, including encoder configuration, GOP structure, buffering, packetization, network transport, decoder behavior, and rendering.

3. P2P & NAT Traversal

This is one of the strongest signals in the job description. You should understand how two devices behind different NATs establish a connection using ICE, STUN, and TURN, including when relay infrastructure becomes necessary.

4. Embedded Linux

Expect deep questions around Linux processes, threads, IPC, memory, networking, device interfaces, debugging, cross-compilation, and resource-constrained systems.

5. Camera Architecture

Prepare the architecture of an IP camera, including sensors, ISP, encoders, SoC multimedia blocks, storage, networking, streaming, cloud connectivity, and remote access.

6. Architecture & Leadership

At 8+ years, the interviewer will likely evaluate how you make architectural decisions—not just whether you can implement code.

Be prepared to explain trade-offs involving latency, bandwidth, CPU, memory, reliability, scalability, security, and hardware limitations.

────────────────────

JOB DETAILS

Original Role Information

Role Overview

ThinkPalm Technologies is looking for a Lead Developer & Software Architect to lead the architecture and development of a next-generation IP camera platform.

The position combines hands-on C/C++ development, embedded Linux engineering, real-time video processing, networking, low-latency streaming, P2P connectivity, and technical leadership.

Responsibilities

System Architecture

Define the software architecture for the IP camera platform
Make technical architecture decisions
Lead architecture reviews
Design scalable and high-performance embedded systems

Embedded Development

Lead development of embedded Linux camera software
Develop high-performance software using C/C++
Work with embedded hardware and SoC capabilities
Optimize resource-constrained systems

Video Pipeline

Design video capture pipelines
Implement video encoding and decoding
Develop real-time streaming
Optimize playback
Improve end-to-end latency
Optimize bitrate and buffering

Streaming & Connectivity

Implement extremely low-latency streaming
Enable fast remote connection establishment
Design P2P connectivity
Implement NAT traversal
Work with ICE, STUN, TURN, signaling, and relay mechanisms

Video Technologies

H.264
H.265
RTSP
RTP
WebRTC
Proprietary streaming protocols

Camera & Hardware Integration

Integrate camera sensors
Work with ISP pipelines
Integrate hardware video encoders
Work with SoC multimedia capabilities
Optimize hardware/software interaction

AI Integration

Integrate AI-based video event detection
Support video analytics
Work with embedded AI/NPUs where applicable

Technical Leadership

Conduct architecture reviews
Conduct code reviews
Make technical decisions
Mentor developers
Collaborate with Hardware teams
Work with Cloud, Mobile, AI, and Product teams

────────────────────

REQUIREMENTS

Programming

Strong C
Strong C++
Hands-on development experience

Embedded

Embedded Linux
Multithreading
High-performance embedded systems
Linux-based camera software

Video

Video encoding/decoding
H.264
H.265
GOP structure
Bitrate management
Buffering
Latency optimization
Real-time streaming

Networking

TCP
UDP
NAT traversal
P2P
ICE
STUN
TURN
Signaling
Relay mechanisms

Camera Technology

Camera sensors
ISP pipelines
Hardware video encoders
Camera SoCs
IP camera architecture

Architecture

System architecture
Performance optimization
Debugging
Technical decision-making
Architecture reviews
Code reviews

Leadership

Developer mentoring
Cross-functional collaboration
Technical ownership

Preferred

GStreamer
FFmpeg
V4L2
libcamera
ONVIF
Embedded AI / NPUs
Object detection
MQTT
Cloud-connected cameras

────────────────────

QUALIFICATIONS

8+ years of relevant engineering experience
Strong C/C++ development
Strong embedded Linux background
Deep real-time video knowledge
Experience with video encoding/decoding
Strong networking fundamentals
Experience with low-latency video systems
Experience with P2P/NAT traversal technologies
Understanding of IP camera architecture
Multithreaded embedded development
Strong debugging and system architecture skills
Technical leadership experience
Ability to mentor developers
Ability to collaborate across Hardware, Cloud, Mobile, AI, and Product teams

────────────────────

POTENTIAL GAPS TO CHECK

C/C++ depth: This is a core requirement; general software development experience will not compensate for weak C/C++.
Embedded Linux: Desktop/server Linux experience alone may not be enough.
Video streaming: You should have practical experience rather than only theoretical codec knowledge.
H.264/H.265: Prepare detailed understanding of encoding parameters, GOP, bitrate, buffering, and latency.
P2P/NAT traversal: ICE/STUN/TURN is a significant differentiator for this role.
Camera experience: Direct IP camera/sensor/ISP/SoC experience would strongly strengthen your profile.
WebRTC: Experience is valuable for modern low-latency streaming systems.
Hardware integration: Lack of experience with camera SoCs or hardware encoders could be a meaningful gap.
GStreamer/FFmpeg: These are listed as advantages, so absence is less critical than the core requirements.
AI video analytics: Useful bonus, particularly for smart-camera platforms.
Architecture leadership: Prepare concrete examples of architecture decisions and technical ownership.

────────────────────

INTERVIEW PREPARATION

Phase 1 — Advanced C/C++

Pointers and memory management
RAII
Smart pointers
Templates
STL
Move semantics
Multithreading
Mutexes
Condition variables
Atomics
Memory synchronization
Performance optimization
C/C++ debugging

Phase 2 — Embedded Linux

Processes and threads
IPC
Signals
Linux networking
Memory management
File descriptors
Device interfaces
Cross-compilation
Linux debugging
CPU/memory profiling
Embedded resource constraints

Phase 3 — Video Processing

Video pipeline architecture
H.264
H.265
I/P/B frames
GOP structure
Bitrate
Keyframes
Encoding profiles
Hardware vs software encoding
Decoding
Buffering
Frame drops
End-to-end latency

Phase 4 — Streaming Protocols

RTP
RTSP
WebRTC
UDP vs TCP
Packet loss
Jitter
Jitter buffers
Adaptive bitrate concepts
Signaling
Real-time streaming architecture

Phase 5 — P2P & Networking

NAT types
NAT traversal
STUN
TURN
ICE
Candidate gathering
Connectivity checks
P2P connection establishment
Relay fallback
UDP hole punching
Connection establishment latency

Phase 6 — Camera & Hardware

Camera sensor pipeline
ISP
Image processing
V4L2
libcamera
Hardware encoders
SoC multimedia blocks
Camera drivers
GStreamer
FFmpeg
ONVIF

Phase 7 — AI & Cloud Connectivity

Edge AI
NPU concepts
Object detection
Video event detection
MQTT
Cloud-connected camera architecture
Edge vs cloud processing
Bandwidth optimization

Phase 8 — Architecture & Leadership

Prepare real examples covering:

Designing an IP camera architecture
Reducing video latency
Solving a difficult streaming problem
Debugging a production camera issue
Optimizing CPU/memory usage
Designing P2P connectivity
Making hardware/software trade-offs
Leading an architecture review
Handling technical disagreements
Mentoring engineers
Coordinating Hardware, Cloud, Mobile, AI, and Product teams

Final Interview Focus

Be ready for questions such as:

How would you architect an end-to-end IP camera platform?
How would you achieve sub-second or ultra-low-latency remote video streaming?
Explain the complete H.264/H.265 streaming pipeline.
What is GOP structure and how does it affect latency?
How would you reduce video latency without significantly increasing bandwidth?
RTSP vs WebRTC — when would you use each?
Explain ICE, STUN, and TURN and how they work together.
How would two cameras establish P2P connectivity when both are behind NAT?
When would you use a TURN relay?
How would you troubleshoot intermittent video freezes?
How would you diagnose dropped frames in an embedded camera?
How would you optimize a C++ video pipeline running on a constrained SoC?
How do hardware encoders differ from software encoders?
Explain the role of the ISP in an IP camera.
How would you integrate an AI object-detection model into a camera pipeline?
How would you design the system for unreliable networks?
How would you balance latency, video quality, CPU usage, and bandwidth?
Describe an architecture decision you made that had significant technical trade-offs.
How do you lead architecture and code reviews while remaining hands-on?

How to apply

Your email app will open with a message ready to send to the employer.

Apply by email