๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฑ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ญ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฑ๐ฑ-๐ญ๐ฌ๐ฌ ๐๐ฃ๐)
Experience: 10+ yrs
Location: Bengaluru
Job Type: Full-time
We are seeking a highly experiencedย Senior / Principal AI Engineerย to design, build, and scale enterprise-grade AI infrastructure, agentic systems, and backend platforms that power production AI applications. This role is ideal for engineers who combine deep expertise in AI, backend engineering, and cloud infrastructure with a passion for building secure, reliable, and scalable AI solutions.
As a Senior / Principal AI Engineer, you will work across AI agents, LLM-powered applications, model routing, inference services, and production infrastructure while contributing to both AI application development and backend platform engineering. You will collaborate closely with product, engineering, and platform teams to transform AI prototypes into production-ready systems with strong observability, governance, and operational excellence.
Requirements
Key Responsibilities
- Design, develop, and maintain scalable AI infrastructure supporting enterprise-grade agentic applications.
- Build intelligent AI workflows involving Retrieval-Augmented Generation (RAG), tool calling, memory, planning, and multi-agent collaboration.
- Develop backend APIs, services, and asynchronous processing systems using Python and Rust.
- Design and implement model routing, inference pipelines, AI gateways, and secure integrations with enterprise systems.
- Build reusable frameworks for agent lifecycle management, workflow orchestration, evaluation, and deployment.
- Develop scalable event-driven architectures for long-running AI and backend workloads.
- Integrate LLMs, vector databases, hosted model providers, and enterprise data sources into production environments.
- Design and optimize MLOps workflows including model deployment, versioning, monitoring, rollback, and continuous evaluation.
- Implement CI/CD pipelines, containerized deployments, Kubernetes orchestration, infrastructure automation, and cloud-native operational practices.
- Monitor production systems using logs, metrics, distributed tracing, and observability tools while continuously improving reliability, security, scalability, and cost efficiency.
- Collaborate with cross-functional engineering teams to define architecture, establish engineering standards, and mentor developers on AI platform best practices.
What Makes You a Great Fit
- 10+ years of experience in software engineering, AI platform engineering, backend development, or distributed systems.
- Strong hands-on expertise inย Pythonย and exposure toย Rustย for building production-grade backend and AI services.
- Proven experience designing and deploying LLM-powered applications, AI agents, or agentic workflows in production.
- Strong understanding of Retrieval-Augmented Generation (RAG), embeddings, vector databases, tool orchestration, and model inference.
- Experience building scalable backend services, REST APIs, asynchronous systems, and distributed architectures.
- Hands-on expertise with Docker, Kubernetes, CI/CD pipelines, cloud infrastructure, and modern DevOps practices.
- Experience working with PostgreSQL, Redis, messaging systems, object storage, and cloud-native infrastructure.
- Strong understanding of AI observability, model evaluation, prompt management, monitoring, and operational best practices.
- Familiarity with AI infrastructure technologies such as model gateways, inference platforms, agent orchestration frameworks, or developer tooling.
- Excellent problem-solving, architectural decision-making, communication, and technical leadership skills with the ability to independently own complex engineering initiatives from design through production.