What You Will Do
We're looking for a talented Software Engineer with enthusiasm for AI/GenAI to join our innovation-focused team building agentic AI systems autonomous, reasoning-driven software agents capable of orchestrating complex tasks across tools, APIs, and data sources.
You'll design and build scalable backend systems that integrate large language models (LLMs) and other AI technologies into production-grade services. This role blends backend engineering, AI integration, and rapid prototyping, with room to learn, experiment, and deliver impactful, scalable solutions.
- Architect and implement distributed services that power AI-driven applications.
- Implement multi-step reasoning pipelines, retrieval-augmented generation (RAG), and domain-specific AI capabilities.
- Build robust APIs, plugins, and integrations enabling AI agents to interact with external systems and data sources.
- Create and refine proof-of-concepts, validating ideas quickly before scaling to production.
- Implement monitoring, evaluation, and logging systems to track AI-powered features in production.
- Work closely with product managers, designers, data scientists, and AI engineers to deliver user-focused solutions.
- Stay curious about emerging AI frameworks, tools, and best practices to improve reasoning, planning, and autonomy in backend services.
Who You Are
- Backend Specialist with a Passion for AI/GenAI, strong systems skills, and eagerness to grow in AI.
- 5+ years in Python /Java/Scala/C#/ Go
- Familiarity with LLM frameworks (LangChain, LangGraph, Haystack, etc.) or willingness to learn.
- Basic understanding of prompt engineering, vector databases, and retrieval-augmented generation (RAG).
- Solid knowledge of cloud infrastructure (AWS, GCP, Azure) and containerization (Docker, Kubernetes).
- Proven track record in building scalable, distributed systems.
- Strong problem-solving, ownership, and collaboration skills.
Nice-to-Have Skills
- Exposure to reinforcement learning, multi-agent systems, or autonomous planning.
- Interest in ML Ops, AI model deployment, and scaling AI workloads.
- Experience with data engineering for AI (ETL pipelines, feature stores, embeddings).
- Any experience building AI-powered platforms or agent-based systems.