You will contribute to building enterprise-grade AI-powered applications, enabling knowledge automation and conversational intelligence. You will collaborate closely with cross-functional teams, ensuring seamless CI/CD integration and DevOps best practices while driving innovation through modern AI/ML frameworks.

Key Responsibilities :

- Architect, design, and implement scalable backend systems using TypeScript /JavaScript (Node.js).

- Lead the design and optimization of data models and indexing strategies in PostgreSQL and/or Elasticsearch for high-volume workloads.

- Build search and analytics pipelines that support 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.

- Partner with AI and platform teams to embed machine learning inference, semantic search, and vector indexing into production workflows.

- Oversee system reliability, data consistency, and fault-tolerant design across distributed microservices.

- Mentor mid-level engineers, drive design reviews, and contribute to long-term platform evolution and scalability roadmap.

- Collaborate with DevOps to design resilient containerized deployments using Docker and Kubernetes.

Required Skills & Experience :

- 8 to10 years of backend engineering experience, ideally in high-scale SaaS or AI product environments.

- Deep expertise in PostgreSQL (query optimization, schema design, indexing) and/or Elasticsearch (clustering, shard management, advanced querying).

- Strong proficiency in TypeScript and 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 in Docker, Kubernetes, and CI/CD (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.

- Experience with Redis, Memcached, or CDN-based caching strategies.

- Familiarity with threat intelligence, anomaly detection, or cybersecurity data systems.