LeadSquared AI Architect – Python, LLM & Multi-Agent Systems

Location:
Bengaluru

Experience:
6–8 Years Department: Engineering – AI/ML Employment: Full-Time About LeadSquared LeadSquared is a leading sales execution and marketing automation platform trusted by 2,000+ businesses globally, including healthcare, education, financial services and real estate. Headquartered in Bengaluru, with offices across the US, UK, UAE and Southeast Asia, we empower sales teams to close faster, smarter and at scale. Our AI team is building next-generation intelligent agents, AI copilots and autonomous workflow automation systems powered by LLMs to transform enterprise business operations. Role Overview We are looking for an experienced AI Architect with a strong background in Python backend engineering, LLM-powered applications and multi-agent orchestration. The ideal candidate would have worked as a Staff Engineer, Principal Engineer or AI Architect, with hands-on experience designing and architecting complex AI systems, building multi-agent frameworks and deploying production-grade AI solutions at scale. This role requires someone who can drive AI architecture and technical strategy while remaining hands-on with coding, system design and implementation. The candidate should have a proven track record of solving real-world business problems through AI, taking agentic systems from experimentation to production and scaling them for enterprise adoption.

Key Responsibilities
• Define and drive the overall architecture for enterprise-grade AI applications, LLM-powered systems and multi-agent platforms.
• Architect and build sophisticated multi-agent systems involving agent orchestration, planning, reasoning, memory management and autonomous decision-making.
• Design scalable backend systems using Python, FastAPI, Django and microservices architecture.
• Build and deploy LLM-powered agents using frameworks such as LangGraph, LangChain, CrewAI, AutoGen or similar.
• Architect advanced RAG pipelines,



knowledge retrieval systems, vector databases and context management frameworks.
• Design agent-to-agent communication, tool calling, function calling and integrations with enterprise APIs, databases and external systems.
• Drive technical decisions around LLM selection, model deployment, fine-tuning, inference optimisation, latency and cost efficiency.
• Establish evaluation frameworks, observability, guardrails and monitoring mechanisms to ensure reliability and accuracy of AI agents.
• Lead the transition of AI prototypes and PoCs into scalable, production-ready enterprise applications.
• Define architectural best practices, engineering standards and reusable AI frameworks across teams.
• Collaborate with Product and Business stakeholders to identify opportunities where AI and agentic workflows can solve complex business problems.
• Provide technical leadership and mentorship to engineering teams while contributing hands-on to critical implementations. Experience & Background
• 6-8 years of overall software engineering experience, with solid Python backend engineering expertise.
• Experience working in Staff Engineer, Principal Engineer, Lead Architect or equivalent technical leadership roles.
• Strong hands-on experience architecting and deploying LLM-powered applications and multi-agent AI systems.
• Proven experience taking complex AI projects from architecture and design through production deployment and scaling. Technical Expertise
• Strong proficiency in Python, FastAPI, Django and backend system design.
• Deep understanding of LLMs, prompt engineering,



context engineering and model integration.
• Hands-on experience with multi-agent orchestration frameworks such as LangGraph, LangChain, CrewAI, AutoGen or equivalent.
• Strong understanding of agentic workflows, autonomous agents, planning, memory, tool calling and function calling.
• Experience designing advanced RAG architectures, embeddings, semantic search and vector databases.
• Experience with OpenAI, Anthropic, Gemini, Llama or other open-source LLMs.
• Strong understanding of distributed systems, microservices, asynchronous programming and API architecture.
• Experience with cloud platforms (AWS, Azure or GCP), Docker and Kubernetes.
• Knowledge of LLM evaluation, observability, security, guardrails and production monitoring. What We Specifically Look For
• Experience architecting and deploying multi-agent AI systems in production, not just POCs or experimental projects.
• Ability to explain complex business problems solved through AI and the architecture implemented.
• Clear understanding of how agents collaborate, communicate, delegate tasks and execute end-to-end workflows.
• Experience defining scalable AI architectures and making build-versus-buy decisions.
• Strong Python backend engineering capabilities with the ability to contribute hands-on to critical code.
• Experience scaling AI applications for large enterprise user bases with measurable business outcomes. Preferred Qualifications
• Experience building enterprise AI platforms, AI copilots or autonomous workflow automation solutions.
• Experience with MCP, agent memory systems and advanced orchestration architectures.
• Exposure to fine-tuning, model optimisation and self-hosted LLM deployments.
• Experience in B2B SaaS, CRM, FinTech, Healthcare or enterprise technology platforms.
• Experience handling large-scale AI workloads with a focus on performance, reliability and cost optimisation.

📌 AI Architect (Bengaluru)
🏢 LeadSquared
📍 Bengaluru