Senior AI Backend Engineer(8-10 YRS)

📌 Senior Backend Engineer | Remote | 8-10 Years

Key Responsibilities:

• Architect, design and implement scalable backend systems using

  TypeScript/JavaScript (Node.js)

  .
• Lead data model and indexing strategy decisions in

  PostgreSQL

  and/or

  Elasticsearch

  for high-volume workloads.
• Build search & analytics pipelines supporting AI/ML and NLP-based content classification and detection.
• Optimize APIs and services for

  high throughput

  ,

  low latency

  , and horizontal scalability across global deployments.
• Establish best practices in code quality, observability, CI/CD, and performance monitoring.
• Collaborate with AI & platform teams to embed ML inference, semantic search and vector indexing into production workflows.
• Ensure system reliability, data consistency and fault-tolerant design across distributed microservices.
• Mentor mid-level engineers, lead design reviews, and contribute to the long-term platform roadmap.
• Partner with DevOps to design resilient containerised deployments using

  Docker

  and

  Kubernetes

  .

Required Skills & Experience:

• 8-10 years of backend engineering experience, ideally in high-scale SaaS or AI product environments.
• Deep expertise in PostgreSQL (query optimisation, schema design, indexing) and/or Elasticsearch (clustering, shard management, advanced querying).
• Strong proficiency in TypeScript/JavaScript (Node.js).
• Proven track record of scaling backend systems to handle millions of transactions and large user bases.
• Solid understanding of system design, caching, load balancing and distributed computing.
• Experience with message queues (Kafka, RabbitMQ, SQS) and event-driven architectures.
• Hands-on experience with Docker, Kubernetes and CI/CD tools (e.g., GitHub Actions, Jenkins, Azure DevOps).
• Working knowledge of cloud platforms (AWS/Azure/GCP), infrastructure-as-code and monitoring stacks (Grafana, Prometheus, ELK).
• Familiarity with API security, authentication and rate limiting for large-scale REST or GraphQL systems.
• Strong problem-solving, debugging and performance profiling capabilities.

Good to Have:

• Exposure to AI/ML system integration, vector databases or semantic search pipelines.
• Experience with data streaming frameworks (Kafka Streams, Flink, Spark Streaming).
• Understanding of zero-downtime release strategies, blue-green deployments and chaos testing.
• Familiarity with Redis, Memcached or CDN-based caching strategies.
• Experience with threat intelligence, anomaly detection or cybersecurity data systems.