AI Engineering Leader — Trinetra AI

Location: Delhi NCR
Role Type: Full time, Leadership
Stage: 0→1 Build and Scale

About Trinetra AI Trinetra AI is building an AI-native platform for next-generation content creation, intelligence, production and decision-making .
The platform brings together Generative AI, multimodal intelligence, video technology, creator workflows, content analytics, production tools and enterprise-grade SaaS/PaaS infrastructure .
We are looking for an AI Engineering Leader who can take this vision from 0→1 , build the core technology stack, create the engineering team, and scale Trinetra into a robust AI platform.
This is not a pure management role. We need a hands-on builder-leader who can move comfortably across AI models, video technology, backend architecture, databases, APIs, cloud infrastructure and frontend applications, and who is willing to prototype or vibe-code when required.

Website - https://trinetraai.co/

What We Are Looking For The ideal candidate combines:
DeepTech AI + GenAI + Video Technology + Full-Stack Architecture + SaaS/PaaS + MediaTech + Startup Execution
We are particularly interested in people who have already built technology products from an early stage and understand the journey from:
Idea → Architecture → Prototype → MVP → Product → Platform → Scale
Startup, founding-team or early-stage engineering experience will be strongly preferred.

Key Responsibilities1. Own Trinetra’s AI and Technology Architecture Define and own the end-to-end architecture across:
Generative AI
LLMs and foundation models
Multimodal AI
Vision-Language Models
AI agents and agentic workflows
RAG and knowledge systems
Embeddings and vector databases
Fine-tuning and model adaptation
Model orchestration
Inference architecture
Model evaluation and observability
AI safety and governance
Cost and latency optimisation
The candidate should understand when to build, fine-tune, integrate, orchestrate or use third-party models , rather than simply adding AI APIs to a conventional product.

2. Build a Scalable SaaS/PaaS Platform Architect Trinetra as a platform, not a collection of disconnected AI tools.
Experience should include:
Multi-tenant SaaS architecture
PaaS architecture
API-first systems
Microservices
Event-driven architecture
Authentication and authorization
RBAC
Developer APIs and SDKs
Usage metering
Subscription and billing architecture
Workflow orchestration
Enterprise integrations
Observability and monitoring
Cloud-native deployment
The long-term architecture should allow Trinetra capabilities to be consumed through both applications and APIs .

3. Deep Understanding of Video Technology A critical requirement for this role is strong knowledge of the video technology stack.
The candidate should understand:
Video ingestion and processing
Encoding, transcoding and compression
Codecs and container formats
FFmpeg or equivalent frameworks
HLS / DASH
Adaptive bitrate streaming
CDN architecture
Video storage and asset management
Shot and scene detection
Frame-level processing
Audio-video synchronization
Rendering pipelines
GPU-based processing
Large-scale media infrastructure
Metadata extraction
Video workflow orchestration
The person should understand the technical and infrastructure implications of operating video-heavy AI products at scale .

4. Lead Generative Video AI Architecture The candidate should have a deep understanding of the evolving Generative Video AI ecosystem .
Relevant areas include:
Text-to-video




Image-to-video
Video-to-video
Character consistency
Reference conditioning
Motion control
Camera control
Lip sync
Voice generation
AI dubbing and localization
Video inpainting and outpainting
AI editing
Storyboard-to-video
Scene generation
Multimodal content understanding
Diffusion and transformer-based architectures
They should be familiar with leading and emerging model ecosystems such as Veo, Sora, Runway, Kling, Seedance, Hailuo, Luma and comparable open-source and proprietary models .
More importantly, the candidate should be able to answer:
Which model should be used for which workflow based on quality, speed, consistency, cost and scalability?
We want someone capable of building a model orchestration layer so Trinetra can intelligently route tasks across different AI models rather than becoming dependent on a single provider.

5. Architect End-to-End AI Video Workflows The candidate should be able to design and scale workflows such as:
Script → Scene Breakdown → Storyboard → Character/World Generation → Video Generation → Voice → Music/SFX → Editing → Quality Control → Final Output
They should understand how to maintain:
Character consistency
Visual continuity
Style consistency
Narrative continuity
Voice consistency
Brand and IP controls
Generation quality
Versioning
Human-in-the-loop workflows
Inference cost control
Production reliability
The candidate should understand that building an AI studio requires much more than connecting multiple APIs.

6. Own Backend and Data Architecture The candidate should be comfortable owning or guiding:
Backend services
APIs
Databases
Data pipelines
Model services
Workflow engines
Caching
Queues
Search infrastructure
Analytics infrastructure
Vector databases
Feature stores
Data warehouses
Object storage
Strong knowledge of SQL, NoSQL, distributed systems, vector databases and large-scale data architecture is important.
The platform will need to manage large volumes of:
Video
Audio
Images
Scripts
Metadata
Embeddings
Model outputs
User behaviour data
Generated assets

7. Understand Frontend Product Engineering The candidate does not need to be a specialist frontend engineer but must understand modern product engineering end-to-end.
Relevant experience includes:
React
Next.js
TypeScript
API-driven applications
AI-native user interfaces
Copilot and chat interfaces
Streaming AI responses
Workflow applications
Media-heavy interfaces
Real-time applications
They should be capable of making informed architectural decisions across the frontend-backend-AI stack .

8. Be Hands-On and Comfortable Vibe-Coding We want a leader who still builds.
The candidate should be comfortable using modern AI-assisted development environments to rapidly create:
Proofs of concept
Internal tools
AI agents
APIs
Product prototypes
Automation
Workflow applications
Technical experiments
Experience with tools such as Cursor, Claude Code, Codex, GitHub Copilot or equivalent AI development environments is highly relevant.
Vibe-coding should be used as a way to improve experimentation velocity ,



while maintaining strong engineering standards for production systems.

9. Lead the 0→1 Journey This is one of the most important requirements.
The candidate should have real experience with:
Selecting the initial technology stack
Designing architecture from scratch
Making build-vs-buy decisions
Building rapid prototypes
Launching MVPs
Managing technical debt
Hiring the initial engineering team
Establishing engineering practices
Iterating with product and users
Scaling infrastructure after product traction
Managing cloud and inference economics
We strongly prefer candidates who have worked in startups, entrepreneurial technology environments or founding teams .

10. Build and Lead the Engineering Organisation The candidate will help build Trinetra’s engineering team across:
AI/ML Engineering
Generative AI Engineering
Video AI Engineering
Backend Engineering
Frontend Engineering
Data Engineering
MLOps
DevOps / Cloud
AI Product Engineering
They should create a culture focused on:
Build → Ship → Measure → Learn → Improve

MediaTech Experience — Strongly Preferred Candidates with experience in MediaTech, OTT, streaming, creator technology, gaming, VFX, post-production technology or AI-video startups will be strongly preferred.
Relevant experience may include:
OTT platforms
Video streaming
AI video platforms
Creator tools
Video editing
Media asset management
Digital studios
VFX / virtual production
Content supply chains
Localization technology
AdTech involving video
Content analytics
The ideal candidate understands both:
How digital media is technically produced and delivered
and
How Generative AI is changing the content production stack.

Technical Understanding We Expect The candidate should have strong working knowledge across a meaningful combination of:
AI / DeepTech LLMs
Generative AI
Multimodal AI
Vision-Language Models
Video foundation models
AI agents
RAG
Embeddings
Vector search
Fine-tuning
Model evaluation
Prompt and context engineering
AI inference optimisation
Backend Python and/or Node.js
REST / GraphQL / gRPC
Microservices
Distributed systems
Event-driven architecture
API architecture
Queues and asynchronous processing
Data PostgreSQL / MySQL
NoSQL
Redis
Vector databases
Data warehouses
Data lakes
Object storage
Data pipelines
Cloud & Infrastructure AWS / GCP / Azure
Docker
Kubernetes
CI/CD
Serverless architectures
Observability
GPU infrastructure
AI inference infrastructure
Frontend React
Next.js
TypeScript
Modern AI-native UX patterns
Video Technology FFmpeg
Encoding and transcoding
Video codecs
HLS / DASH
CDN architecture
GPU video processing
Media pipelines
Asset management
Video metadata
Scene and shot processing
What Will Differentiate a Strong Candidate Preference will be given to candidates who have:
Built an AI or DeepTech product from 0→1
Built or scaled a SaaS/PaaS platform
Worked on Generative AI products
Worked with video foundation models
Built or managed video infrastructure
Strong backend and database architecture experience
Experience with multimodal AI
Experience with GPU/inference infrastructure
Experience orchestrating multiple AI models
Worked in MediaTech / OTT / creator-tech / AI-video
Startup or founding-team experience
Built engineering teams
Remained technically hands-on
Personally shipped production code
Strong product thinking
Strong understanding of AI unit economics

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