Overview

We are looking for a hands-on Lead Engineer AI/ML & Backend Systems to build and scale AI-powered products and the backend platforms behind them.

Responsibilities

• Lead the design, development, and production deployment of AI/ML and Generative AI solutions.
• Build LLM-powered applications using prompt engineering, RAG, embeddings, structured outputs, tool/function calling, and agentic workflows where appropriate.
• Design and build scalable backend services, APIs, microservices, and asynchronous processing systems.
• Develop production-grade services using Python and contribute to or work with existing Go-based services.
• Design and operate event-driven systems using Kafka, RabbitMQ, Pub/Sub, or equivalent platforms.
• Apply distributed-system patterns such as idempotency, retries, timeouts, ordering, backpressure, failure recovery, and eventual consistency.
• Build and manage data pipelines and work with relational databases, caches, search systems, and vector stores as required.
• Define systematic evaluation of AI outputs for accuracy, relevance, groundedness, consistency, and regression.
• Own production systems end to end: design, development, deployment, monitoring, optimization, and incident resolution.
• Build strong observability using metrics, logs, distributed tracing, dashboards, and alerts.
• Track and improve system availability, latency, throughput, error rates, AI quality, queue health, and infrastructure efficiency.
• Lead architecture reviews, code reviews, technical decisions, and engineering standards.
• Mentor engineers and collaborate with Product, Data, DevOps/SRE, and other engineering teams.

Qualifications

• 4-7 years of relevant engineering experience with strong hands-on software/backend development experience.
• Strong proficiency in Python and experience building production-grade services.
• Hands-on experience developing and deploying AI/ML and LLM-based applications.
• Strong understanding of RAG, embeddings, prompt/context engineering, AI evaluation, and modern LLM application patterns.
• Working knowledge of Go (Golang) with the ability to understand, debug, and contribute to Go services.
• Strong understanding of backend architecture, APIs, microservices, concurrency, caching, and database design.
• Strong understanding of distributed systems and event-driven architecture.
• Hands-on experience with Kafka, RabbitMQ, Pub/Sub, or similar messaging/streaming platforms.
• Strong proficiency in SQL and relational databases.
• Strong production-debugging and root-cause-analysis skills.
• Strong hands-on experience with observability and monitoring, including metrics, logs, tracing, dashboards, and alerting.
• Experience with tools such as Prometheus, Grafana, OpenTelemetry, ELK/Kibana, Datadog, New Relic, or equivalent.
• Ability to design systems for scalability, reliability, performance, maintainability, and cost efficiency.
• Experience mentoring engineers and driving technical design and code quality.
• Strong hands-on development experience with Go.
• Experience with LangGraph, LangChain, LlamaIndex, or equivalent AI orchestration frameworks.
• Experience with vector databases/search such as pgvector, FAISS, Qdrant, Pinecone, or equivalent.
• Exposure to MLOps/LLMOps, model/prompt versioning, AI regression testing, and guardrails.
• Experience with Redis, Elasticsearch/OpenSearch, Docker, Kubernetes, and cloud platforms.
• Experience building high-scale, high-throughput distributed systems.
• 57 years of relevant experience across backend engineering, AI/ML, or applied AI.
• B.E./B.Tech/M.E./M.Tech in Computer Science, IT, AI/ML, Data Science, or a related discipline, or equivalent practical experience.