Engineering Team Lead – Brahma Studio
•Location: Remote (Timezone UTC+0 - UTC+3)
•Team Size: 8–10 Direct Reports
About Brahma AI
Brahma AI operates at the intersection of enterprise Media Asset Management (MAM) and cutting-edge generative media. We build and scale industry-leading generative AI models, including hyper-realistic digital humans (ATMAN) and multilingual voice synthesis (VAANI), for world-class enterprise clients in entertainment, sports, healthcare, and retail.
Role Overview
We are looking for a delivery-focused Engineering Team Lead to manage our Brahma Studio engineering team. Reporting directly to the VP of Engineering, you will lead a team of 8–10 backend and fullstack engineers dedicated to building and scaling our core generative media SaaS platform.
This is a leadership-first role where team management, execution, and delivery take priority. You will actively contribute to technical decisions, translate product roadmaps into realistic engineering milestones, and drive the team to hit target dates. You should be capable of writing code but this will occupy less than 20% of your time.
Key Responsibilities
1. Delivery, Process & Execution Leadership (50%)
- Roadmap & Delivery Ownership: Translate product roadmaps and PRDs into clear engineering milestones, maintaining predictable release schedules.
- Agile Execution & Cadence: Establish efficient stand-ups and execution workflows that align the team with target delivery dates.
- Risk Management & Unblocking: Proactively identify technical risks, clear blockers, and make pragmatic trade-offs to keep code moving to production.
- People Leadership & Growth: Manage and mentor 8–10 backend and fullstack engineers through 1:1s and structured career development.
2. Quality, Standards & Operational Discipline (35%)
- Technical Architecture & Standards: Partner with the System Architect to shape technical vision, enforce code quality benchmarks, and ensure scalable design.
- Shift-Left Quality & Security: Enforce engineering rigor across code reviews, CI/CD pipelines, and shift-left quality and security practices.
- Targeted Contributions: Build internal PoCs, demo tools, and custom AI skills to improve team efficiency and raise engineering quality.
- Developer Velocity: Integrate AI-assisted development tools into daily workflows to streamline routine tasks and maximise throughput.
- Observability & Health: Ensure services feature comprehensive logging, monitoring, and alerting (DataDog) for operational stability.
3. Cross-Functional Alignment & Escalation (15%)
- Engineering Escalation: Serve as the primary point of contact for Studio engineering, resolving operational friction and resource constraints.
- Stakeholder Alignment: Collaborate with Product Managers, the System Architect, and leadership to align core platform development with commitments.
Must Haves
- Leadership & People Management: 3+ years of experience managing engineering teams of 8–10 direct reports, with a strong track record of structured mentorship, regular 1:1 cadences, and predictable delivery.
- SaaS Delivery Execution: Proven ability to break complex platform requirements into iterative technical deliverables and maintain consistent delivery schedules.
- Pragmatic Technical Leadership: Strong background in technical decision-making, code reviews, and mentorship.
- AI Tooling & Developer Velocity: Experience applying AI-assisted development tools and LLM integration workflows to increase team output and streamline routine engineering tasks.
- Core Backend Stack: Solid expertise in server-side Python, RESTful API design, microservices architectures, relational databases (PostgreSQL), and asynchronous/caching layers (Redis, background workers).
- Operational Mindset: Practical experience with containerized environments (Docker, Kubernetes), CI/CD automation, and cloud observability platforms.
- Direct Communication: Excellent communication skills, with a track record of setting clear expectations, addressing scope creep early, and communicating technical trade-offs candidly.
Nice to Have
- AI & Media Concepts: Understanding of media processing pipelines (FFmpeg, video encoding) or AI model serving (latency, inference).
- Cloud Infrastructure: Exposure to Google Cloud Platform (GCP) or multi-cloud architecture.
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