SDE 2 - Backend & Gen AI

Responsibilities:

• System Architecture: Research, design, and build highly reliable, distributed, available, and scalable platform services using Java.
• GenAI and LLM Integration: Build applied GenAI applications such as semantic search, automated beauty advisory bots, and multi-agent frameworks using open-source LLMs and APIs.
• RAG and Vector Data: Implement RAG workflows and manipulate vector databases (e. g., Pinecone, Milvus, PGVector) for high-performance context retrieval.
• Latency Optimisation: Optimise application architectures, databases, and LLM orchestration layers to minimise token-generation latency while keeping high throughput.
• Collaboration: Partner with core product managers, AI/ML research data scientists, and design teams to take features from a prompt-engineered prototype into robust enterprise production.
• Engineering Best Practices: Champion clean code, extensive Unit/Integration testing, CI/CD pipelines, and mentor junior developers in the team.

Requirements:

• Core Backend: Strong expertise in Java (8 or above), Spring Boot, Microservices, and RESTful APIs.
• Gen AI Stack: Proven experience with LLM frameworks like LangChain, LlamaIndex, or AutoGen, alongside basic proficiency in Python for running ML scripts or AI orchestration.
• Database Mastery: Solid hands-on knowledge of relational databases (MySQL/PostgreSQL) and NoSQL stores (Redis, MongoDB, or Cassandra).
• Foundational CS: Exceptional command over Data Structures, Algorithms (DSA), Object-Oriented Design Patterns, and concurrency.
• Cloud and Infrastructure: Hands-on experience with cloud deployment via AWS (EC2, S3, RDS) or similar platforms, using Docker / Kubernetes.
• AI Tooling (Good to have): Acquaintance with cloud AI frameworks like AWS Bedrock or OpenAI Enterprise APIs.

Skills

AutoGen, Java, LLM, LangChain, LlamaIndex, Spring, Spring Boot