Full Stack AI Engineer - Python/TypeScript/Node.js

Position : Full stack Developer

Qualification : Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or related field.

Years of Experience :

• 5+ years of experience in software engineering, AI/ML engineering, or full-stack development, with hands-on ownership of building and deploying production-grade applications or platforms.

• 4+ years of focused experience building and deploying AI-centric systems.

• 2+ years of hands-on experience with LLM-based agents, autonomous workflows, or multi-agent orchestration.

Employment Details : Permanent Full Time.

Location : Bangalore - Bellandur.

Number of posts : 1.

Gender : Male / Female.

Selection Process :

• Total 3 Technical rounds.

• 2 rounds evaluation with LV (we can plan to take this together based on panel availability) & 1 with client.

Job Role & Responsibility :

• Design and build stateful, multi-agent AI systems using modern orchestration frameworks, enabling scalable and reliable workflows for insights generation and synthesis.

• Collaborate with Product, Research, and business stakeholders to translate requirements into end-to-end AI solutions, from proof of concept through evaluation and production deployment.

• Solid understanding of Retrieval-Augmented Generation (RAG), including hybrid search, re-ranking, and advanced retrieval techniques.

• Implement evaluation frameworks and pipelines (e.g., LLM-as-a-Judge, automated benchmarks) to measure system performance, reliability, and quality before and after release.

• Develop and maintain scalable backend services for high-throughput, low-latency workloads, while contributing to lightweight frontend components to deliver functional prototypes and internal tools.

• Optimize batching, streaming, caching, and request orchestration in distributed and async environments.

• Improve production systems across latency, throughput, reliability, observability, and unit economics.

• Partner with infrastructure teams to leverage GPU-enabled and cloud-native environments effectively.

• Establish monitoring, tracing, and observability practices for complex AI systems, ensuring performance, reliability, and debuggability in production.

• Develop reusable platform components, MCPs / APIs, and best practices for AI application development.