Senior AI Engineer

We're looking for a Senior AI Engineer who can independently own the full lifecycle of generative AI systems from architecture to production deployment. You'll build multi-agent workflows, RAG pipelines, and scalable async backends, with real ownership over the quality, performance, and reliability of what you ship.

Responsibilities:

• Architect and build agentic AI systems using LangGraph, with a focus on state persistence and error recovery.
• Design and optimize RAG pipelines with advanced chunking, hybrid search, re-ranking, and vector DBs.
• Build high-concurrency, async Python backends (FastAPI) for AI workloads at enterprise scale.
• Take ownership of containerization, cloud deployment, and observability for the services you build.
• Work hands-on across AI modalities, vision, audio, and text, as the product demands.
• Own feature delivery end-to-end: design, build, deploy, and production support, with minimal supervision.
• Contribute to system design and architecture conversations, not just implementation.
• Debug and resolve performance issues, bugs, and production incidents.
• Mentor junior engineers through reviews, pairing, and day-to-day guidance.
• Collaborate cross-functionally with product and engineering to keep AI solutions tied to real business goals.

Expectation:

• Advanced, hands-on experience deploying RAG or agent-based systems with LangGraph orchestration.
• Expert-level command of async Python and scalable backend system design.
• A production-first mindset, you care about what happens after the demo.
• Comfort owning releases and supporting what you ship in production.
• Strong grasp of performance, scalability, and reliability fundamentals.
• Deep obsession with automation, DevOps, OOP, and SOLID principles.
• Ability to work with minimal hand-holding and strong judgment on trade-offs.

Requirements:

• 3-5 years of professional software engineering experience, with a strong focus on generative AI/LLM-based applications.
• Proven track record of deploying complex GenAI products to production (not just POCs).
• Strong hands-on expertise in Python, FastAPI, and async programming.
• Experience with LangChain/LangGraph, RAG architectures, and agentic AI frameworks.
• Solid understanding of REST architecture, CI/CD, Docker, and debugging production issues.
• Comfortable working with vector databases and Elasticsearch for retrieval.

Nice to Have:

• Exposure to React/Next.js for AI-enabled front ends.
• Experience with Kubernetes, Jenkins, or Infrastructure-as-Code.
• Familiarity with cloud platforms (AWS/GCP/Azure).
• Exposure to Redis, Kafka/RabbitMQ.