AI Engineering Leader — Trinetra AI

Location: Delhi NCR

Role Type: Full-time, Leadership

Stage: 0→1 Build and Scale

About Trinetra AITrinetra 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 ForThe 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 ArchitectureDefine 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.

- Build a Scalable SaaS/PaaS PlatformArchitect 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.

- Deep Understanding of Video TechnologyA 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.

- Lead Generative Video AI ArchitectureThe 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.

- Architect End-to-End AI Video WorkflowsThe 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.
- Own Backend and Data ArchitectureThe 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

- Understand Frontend Product EngineeringThe 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.

- Be Hands-On and Comfortable Vibe-CodingWe 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.

- Lead the 0→1 JourneyThis 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.

- Build and Lead the Engineering OrganisationThe 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 PreferredCandidates 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 ExpectThe 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 Robust CandidatePreference 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

📌 AI Program Manager (New Delhi)
🏢 Bullet Microdrama OTT
📍 New Delhi