Lead AI Engineer (Up to 140LPA)

🚀 What we're building

CodeRound AI matches the top 5% of tech talent with some of the fastest-growing VC-backed AI startups across Silicon Valley, UAE, and India.

Candidates apply once and unlock up to 20 interview opportunities with leading AI-native startups building the future of software, infrastructure, and intelligence systems.

🧩 What you'll do

• Lead the design, development, and delivery of AI-powered products and platform capabilities.
• Architect and build agentic AI systems leveraging LLMs, autonomous workflows, and intelligent decision-making frameworks.
• Design and implement multi-agent orchestration frameworks, agent communication patterns, and workflow automation systems.
• Build memory, reasoning, tool-calling, and context management capabilities into AI applications.
• Design and develop knowledge graphs, ontologies, and semantic retrieval systems to power intelligent workflows.
• Architect scalable backend systems, APIs, and cloud-native infrastructure for production AI applications.
• Drive technical architecture, system design, and engineering best practices across the organization.
• Collaborate with product, design, and business stakeholders to define and execute the product roadmap.
• Mentor engineers and provide technical leadership across AI and platform engineering initiatives.
• Optimize AI system performance, reliability, scalability, and operational efficiency.
• Implement monitoring, evaluation, observability, and deployment pipelines for AI systems.
• Research, evaluate, and adopt emerging AI technologies, frameworks, and agent architectures.
• Ensure high standards for security, maintainability, testing, and production readiness.

✅ You're a great fit if you

• Have 4+ years of software engineering experience.
• Possess strong Python development skills and backend engineering fundamentals.
• Have hands-on experience building and deploying production-grade AI applications.
• Have significant experience designing and implementing Agentic AI systems.
• Have worked extensively with multi-agent orchestration and workflow design.
• Have experience building agent memory systems and long-context workflows.
• Have hands-on experience with tool-calling architectures and external system integrations.
• Have hands-on experience with frameworks such as LangGraph or similar agent orchestration frameworks.
• Have experience building or working with knowledge graphs, ontologies, semantic data models, or graph-based reasoning systems.
• Understand concepts such as RAG, memory management, planning, evaluation, agent reliability, and autonomous decision-making.
• Have experience building scalable backend systems, APIs, and cloud-native applications.
• Have strong system design, architecture, and problem-solving skills.
• Are comfortable working in fast-paced startup environments with high ownership and autonomy.
• Can collaborate effectively across engineering, product, and business teams.

⚡ Nice to have

• Deep expertise in multi-agent systems and autonomous workflow automation.
• Experience designing long-term memory, episodic memory, and knowledge management systems for AI agents.
• Experience building AI applications powered by knowledge graphs, graph databases, or ontology-driven architectures.
• Experience with AI evaluation frameworks, observability systems, and agent performance monitoring.
• Experience with vector databases, graph databases, and advanced retrieval architectures.
• Experience with MLOps, model deployment, versioning, and monitoring.
• Experience with Docker, Kubernetes, and modern cloud infrastructure.
• Experience hiring, mentoring, and scaling engineering teams.
• Prior experience working in early-stage startups or high-growth environments.
• Experience building AI products used at scale by thousands of users.