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Lead Backend Engineer - AI

Pocket FM

Bengaluru, Karnataka, India

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.

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