Member of Technical Staff - Applied AI

We are looking for a Lead to join our AI/ML Platform team. In this role, you will lead the design, development, and scaling of backend systems that power AI-driven features across Pocket FM. You will work closely with AI/ML engineers, product teams, and backend engineers to build reliable, scalable, and high-performance services for LLM-based workflows, retrieval systems, RAG pipelines, and AI infrastructure. This role requires strong hands-on engineering skills, architectural thinking, and the ability to mentor engineers while setting high standards for code quality and system reliability.
We are looking for someone who is deeply hands-on, takes ownership, and can lead backend initiatives from design to production. The ideal candidate should be comfortable working at the intersection of backend engineering and AI systems, while also mentoring engineers and raising the technical bar for the team. You should be excited about building scalable AI-powered platforms that impact millions of users globally
Responsibilities:
Lead the design, development, and maintenance of backend services, APIs, and microservices using Python or Go.
Architect and scale backend systems that support AI-powered features, LLM workflows, inference pipelines, and retrieval-based systems.
Own the implementation of retrieval systems, vector search, and RAG pipelines, including integrations with vector databases.
Collaborate with AI/ML engineers to integrate LLM APIs, model inference workflows, embeddings, prompt orchestration, and evaluation pipelines.
Drive technical design discussions, architecture reviews, and backend best practices across the AI/ML platform.
Build highly available, secure, performant, and scalable backend components for production-grade AI systems.
Integrate backend services with cloud infrastructure, databases, caching layers, queues, storage systems, and observability platforms.
Establish engineering guardrails for code quality, maintainability, performance, and reliability.
Use AI-assisted development practices responsibly to improve productivity while ensuring strong review, testing, and quality standards.
Mentor junior and mid-level engineers through code reviews, design reviews, documentation, and technical guidance.
Improve CI/CD workflows, automated testing, deployment practices, monitoring, and incident response processes.
Partner with product, data, AI/ML, and platform teams to translate business and AI requirements into scalable backend solutions.
Identify system bottlenecks and proactively improve performance, reliability, security, and developer productivity.
Requirements:
Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
6-9 years of backend engineering experience, with strong hands-on expertise in Python or Go.
Proven experience designing and building scalable backend services, APIs, and distributed systems.
Strong understanding of backend concepts such as microservices, REST APIs, databases, caching, message queues, and service-to-service communication.
Experience with backend frameworks such as FastAPI, Flask, Gin, Echo, or similar.
Strong knowledge of SQL and NoSQL databases such as MySQL, PostgreSQL, MongoDB, Cassandra, or ScyllaDB.
Experience with caching systems such as Redis.
Good understanding of cloud infrastructure on AWS, GCP, or Azure.
Experience with containerization and deployment using Docker; Kubernetes exposure is a plus.
Strong understanding of CI/CD practices, automated testing, monitoring, logging, and observability.
Experience leading technical projects and mentoring engineers.
Strong problem-solving skills with the ability to operate in a fast-paced, high-ownership environment.
Interest or experience in AI systems, LLMs, RAG pipelines, and AI infrastructure.
Nice-to-Have Skills:
Experience with vector databases such as Pinecone, FAISS, Weaviate, Milvus, or similar.
Hands-on experience building RAG pipelines, semantic search, ranking systems, or information retrieval platforms.
Familiarity with LLM APIs such as OpenAI, Anthropic, Gemini, or similar.
Experience with AI orchestration frameworks such as LangChain, LlamaIndex, or similar.
Exposure to model inference infrastructure, embedding pipelines, prompt management, or evaluation frameworks.
Experience with observability tools such as Prometheus, Grafana, Datadog, ELK, or OpenTelemetry.
Experience with event-driven architectures, streaming systems, or queueing platforms such as Kafka, Pub/Sub, SQS, or RabbitMQ.
Prior experience working in consumer internet, content, media, audio, entertainment, or AI-first products.

Skills
API, Backend, Distributed Systems, Embeddings, Go, Golang, LLM, Microservices, Python, RAG, REST, Retrieval, Vector Search

Experience
6-10 yrs