Backend Engineer (AI & Infrastructure)

Backend_Engineer_AI_JD

Backend Engineer (AI & Infrastructure) Full-time · Engineering · 1–3 Years Experience · Onsite/ Hybrid

About the Role We are looking for a curious and hands-on Backend Engineer who sits at the intersection of robust systems engineering and applied AI. You will design and build scalable backend services, architect AI-powered agent workflows, and own the infrastructure that keeps them running reliably in production. This is an early-career role with high ownership, ideal for someone who loves going deep on low-level design as much as they love experimenting with LLMs and vector databases.

Backend Engineering Design and build scalable REST and gRPC APIs using Node.js, Python, or Go Apply solid low-level design patterns: SOLID principles, design patterns, clean

architecture Work with relational (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB)

databases Write efficient, testable, and well-documented code with async/concurrent

programming Participate in code reviews and contribute to engineering best practices

Distributed Systems & Real-Time Communication Build event-driven architectures using Apache Kafka for high-throughput data

pipelines Implement caching and pub/sub messaging with Redis Develop real-time features using WebSockets and Server-Sent Events (SSE) Work with message queues such as RabbitMQ or AWS SQS Apply rate limiting, throttling, and backpressure strategies in production systems

AI Agents & Large Language Models Integrate with LLM APIs (OpenAI, Anthropic Claude, Google Gemini) for

production use cases Build autonomous agent workflows using patterns like ReAct, Plan-and-Execute,

and multi-agent hierarchies Design and manage tool/function calling, context window budgeting, and memory

strategies

Develop Retrieval-Augmented Generation (RAG) pipelines end-to-end Work with agent frameworks such as LangChain or LlamaIndex Apply prompt engineering techniques for reliability and consistency

Vector Databases & Semantic Retrieval Hands-on experience with vector databases: Pinecone, Weaviate, Qdrant, or

ChromaDB Understand embedding models and how to select, generate, and index them

effectively Implement chunking, indexing, and hybrid search strategies (BM25 + dense

retrieval) Design retrieval pipelines optimised for accuracy, latency, and cost

Infrastructure & Cloud Containerise services with Docker and understand Kubernetes for orchestration

basics Hands-on experience with at least one major cloud provider: AWS, GCP, or

Azure Work with serverless compute (Lambda, Cloud Functions) and managed services Exposure to Infrastructure as Code tools, such as Terraform or AWS CDK

CI/CD & Developer Experience Build and maintain CI/CD pipelines using GitHub Actions or GitLab CI Manage Docker image builds, container registries, and automated deployments Apply deployment strategies: blue-green deployments, canary releases, and

rollback procedures Handle secrets and environment configuration management securely Write automated tests (unit, integration, end-to-end) as part of the pipeline

Nice to Have Experience with observability tooling (Datadog, Grafana, OpenTelemetry),

especially for LLM latency and token cost monitoring Familiarity with guardrails, output validation, and safety layers for AI agents Exposure to fine-tuning or LoRA workflows on open-source models Knowledge of streaming architectures: chunked responses, token streaming to

the frontend API security best practices: OAuth 2.0, JWT, RBAC Open-source contributions or personal AI projects on GitHub

What We Value Ownership mindset, you take a problem from an ambiguous brief to production

without hand-holding Clear written communication, especially for async and distributed teams Opinionated about code quality and design, but pragmatic about trade-offs Stays current with the fast-moving AI ecosystem without getting distracted by

hype Curiosity and a habit of building things to learn side projects, experiments, and

open source count

Experience range: 1–3 years. We care more about depth and curiosity than years on a résumé. A strong GitHub profile or portfolio with real-world projects will weigh heavily in our evaluation.

• Backend Engineer (AI & Infrastructure)
  • About the Role
  • Backend Engineering
  • Distributed Systems & Real-Time Communication
  • AI Agents & Large Language Models
  • Vector Databases & Semantic Retrieval
  • Infrastructure & Cloud
  • CI/CD & Developer Experience
  • Nice to Have
  • What We Value

. Skillset Required: Backend Engineering, scalable REST APIs, scalable gRPC APIs, Node.js, Python, Go, low-level design patterns, SOLID principles, design patterns, clean architecture, relational databases, PostgreSQL, MySQL, NoSQL databases, MongoDB, DynamoDB, async programming, concurrent programming, code reviews, engineering best practices, Distributed Systems, Real-Time Communication, event-driven architectures, Apache Kafka, caching, pub/sub messaging, Redis, real-time features, WebSockets, Server-Sent Events, SSE, message queues, RabbitMQ, AWS SQS, rate limiting, throttling, backpressure strategies, AI Agents, Large Language Models, LLM APIs, OpenAI, Anthropic Claude, Google Gemini, autonomous agent workflows, ReAct, Plan-and-Execute, multi-agent hierarchies, tool/function calling, context window budgeting, memory strategies, Retrieval-Augmented Generation, RAG pipelines, agent frameworks, LangChain, LlamaIndex, prompt engineering, Vector Databases, Pinecone, Weaviate, Qdrant, ChromaDB, embedding models, chunking, indexing, hybrid search strategies, BM25, dense retrieval, retrieval pipelines, Infrastructure, Cloud, Docker, Kubernetes, AWS, GCP, Azure, serverless compute, Lambda, Cloud Functions, managed services, Infrastructure as Code, Terraform, AWS CDK, CI/CD pipelines, GitHub Actions, GitLab CI, Docker image builds, container registries, automated deployments, deployment strategies, blue-green deployments, canary releases, rollback procedures, secrets management, environment configuration, automated tests, unit tests, integration tests, end-to-end tests, observability tooling, Datadog, Grafana, OpenTelemetry, guardrails, output validation, safety layers, fine-tuning, LoRA workflows, streaming architectures, chunked responses, token streaming, API security, OAuth 2.0, JWT, RBAC, open-source contributions, GitHub, Ownership mindset, written communication, async communication, distributed teams, code quality, pragmatic trade-offs, AI ecosystem knowledge, curiosity, side projects, experiments, open source projects