Member of Technical Staff - Applied AI (Backend)

We're looking for a Backend Engineer to join our AI/ML platform team. In this role, you'll work closely with experienced engineers to build and support backend services that power AI-driven features.

Responsibilities:

• Assist in developing and maintaining backend services, APIs, and microservices using Python or Go.
• Support the implementation of AI-based systems and solutions, including working with LLMs (speech, text, image/video generation), embeddings, RAGs, and vector databases.
• Build and optimize backend components that support AI-powered features at scale.
• Participate in code reviews, documentation, and CI/CD workflows and mentor juniors to do so.
• Set standard practices for code quality and guardrails to ensure bug-free and maintainable codes
• Contribute to improving performance, reliability, and security of backend services.
• Integrate backend services with cloud infrastructure, databases, caching, and storage systems.
• Collaborate with AI/ML engineers to integrate LLM APIs and support inference workflows.
• Write clean, maintainable, and well-tested code following engineering best practices.
• Define and monitor GenAI metrics, including latency, token consumption, hallucination rates, and retrieval quality.

Requirements:

• Bachelor's or master's degree in computer science, software engineering, or a related field.
• Experience with Python or Go through coursework, projects, or internships.
• Basic understanding of backend concepts such as APIs, databases, and microservices.
• Familiarity with backend frameworks like FastAPI, Flask, Gin, or Echo is a plus.
• Understanding of RESTful APIs; exposure to gRPC or GraphQL is helpful but not required.
• Experience with databases and caching systems, including SQL (MySQL, PostgreSQL), NoSQL (MongoDB, Cassandra, Scylla), and Redis for caching.
• Interest in AI systems, LLMs, and backend infrastructure.
• Strong problem-solving skills and eagerness to learn in a fast-paced environment.

Nice-to-Have Skills:

• Exposure to vector databases (Pinecone, FAISS, Weaviate) or information retrieval concepts.
• Familiarity with LLM APIs (OpenAI, Anthropic, etc. ) through side projects or coursework.
• Experience using Docker or basic containerization concepts.
• Awareness of cloud platforms (AWS, GCP, or Azure) from labs or projects.
• Knowledge of CI/CD pipelines, monitoring, or observability tools (Prometheus, Grafana, Datadog).
• Interest in AI orchestration frameworks such as LangChain or LlamaIndex.
• Hands-on experience with RAG, vector search, agentic systems, and LLM orchestration frameworks.
• Strong understanding of distributed systems, caching, observability, and cloud-native architectures.