Pocket FM
Website:
pocketfm.com
Job details:
About Pocket Entertainment
At Pocket Entertainment, we're rebuilding entertainment from the ground up with AI at the core.
While most companies use AI to optimize existing workflows, we're reimagining how stories are created, localized, distributed, and monetized. Our ambition is to build the world's largest AI-native entertainment platform—one that empowers creators to build global franchises across multiple formats.
Today, Pocket FM reaches over 130 million users worldwide, with strong traction across the US and Europe. Our platform hosts 100,000+ hours of storytelling, generates an ARR of ₹2,000 Cr+, and continues to pioneer AI-powered content creation and distribution at scale.
If you're excited about solving complex engineering problems that shape the future of AI and entertainment, this is where you'll do the most meaningful work of your career.
The Opportunity
We're looking for a Lead Backend Engineer to build the backend infrastructure powering AI across Pocket Entertainment. You'll own the systems behind large-scale LLM applications, retrieval pipelines, model serving infrastructure, vector search, and production AI workflows used by millions of users globally.
This isn't a traditional backend role. You'll work at the intersection of distributed systems and modern AI, partnering closely with AI researchers, ML engineers, and product teams to build infrastructure that enables the next generation of AI-powered storytelling. Beyond building systems, you'll influence architecture, raise engineering standards, mentor engineers, and help define how our AI platform evolves.
What You'll Own
- Design and build highly scalable backend services using Python or Go
- Architect distributed systems powering LLM applications, inference pipelines, RAG workflows, and retrieval infrastructure
- Build production-grade APIs, microservices, and backend platforms supporting AI products across Pocket
- Develop retrieval systems, semantic search, vector database integrations, and embedding pipelines
- Partner with AI engineers to productionize LLM workflows, prompt orchestration, model inference, and evaluation systems
- Drive backend architecture decisions with a strong focus on scalability, reliability, security, and developer experience
- Improve platform observability, deployment pipelines, testing, and operational excellence
- Mentor engineers through design reviews, code reviews, and technical leadership
- Champion engineering best practices while leveraging AI-assisted development responsibly
What Makes You a Great Fit
- 6–9 years of backend engineering experience building large-scale distributed systems
- Strong hands-on expertise in Python or Go
- Proven experience designing scalable APIs, backend platforms, and microservice architectures
- Strong understanding of distributed systems, databases, caching, queues, and service communication
- Experience with relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, Cassandra, or ScyllaDB
- Experience building cloud-native applications on AWS, GCP, or Azure
- Familiarity with Docker, Kubernetes, CI/CD pipelines, monitoring, and production operations
- Track record of leading technical initiatives and mentoring engineers
- Excellent problem-solving ability with a bias for ownership and execution
You'll Stand Out If You Have
- Built production applications using LLMs or Generative AI
- Experience with RAG architectures, semantic search, retrieval systems, or vector databases such as Pinecone, Weaviate, Milvus, or FAISS
- Worked with OpenAI, Anthropic, Gemini, or similar foundation models
- Experience with LangChain, LlamaIndex, or AI orchestration frameworks
- Built inference infrastructure, embedding pipelines, or model evaluation systems
- Experience with Kafka, Pub/Sub, RabbitMQ, or other event-driven architectures
- Previously worked on large-scale consumer internet or AI-first products
What Success Looks Like
You're someone who enjoys building systems from first principles and taking them all the way to production. You care about engineering quality as much as shipping quickly, and you're comfortable operating in an environment where backend engineering and AI intersect every day.
You'll help build the platform that powers AI experiences for millions of users while raising the technical bar for the entire engineering organization.
Click on Apply to know more.