Bullet Microdrama OTT
Website:
bullet.audio
Company:
https://www.linkedin.com/company/bulletmicrodrama
Seniority: Not Applicable
Industries: Broadcast Media Production and Distribution
Job details:
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
Click on Apply to know more.
