Senior AI Engineer

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 at the forefront of integrating cutting-edge large language model capabilities into enterprise workflows building intelligent agents, copilots, and automation systems that redefine how businesses operate.

Role Overview

We are looking for a Senior AI Engineer with hands-on experience building LLM-powered agents and agentic AI systems. You will design, develop, and deploy autonomous AI pipelines that solve complex, multi-step business problems — from lead qualification and follow-up automation to intelligent CRM workflows and beyond.

This role is ideal for someone who is deeply excited about the frontier of AI, can move fast, and wants their work to directly impact millions of sales professionals worldwide.

Key Responsibilities

• Design and build LLM-powered agentic systems using frameworks such as LangChain, LlamaIndex, AutoGen, or CrewAI to automate complex, multi-step workflows.
• Develop and maintain Retrieval-Augmented Generation (RAG) pipelines with vector databases (Pinecone, Weaviate, Chroma, pgvector) for domain-specific knowledge grounding.
• Build and integrate tool-use and function-calling capabilities into AI agents, enabling dynamic interaction with internal APIs, databases, and third-party services.
• Implement prompt engineering strategies including chain-of-thought, few-shot prompting, and structured output parsing to ensure reliable agent behavior.
• Design evaluation frameworks and observability pipelines (LangSmith, Helicone, custom metrics) to monitor agent performance, accuracy, and cost.
• Collaborate with product, sales, and domain teams to translate business requirements into AI-driven solutions and features.
• Optimize LLM inference for latency and cost using techniques like caching, model distillation, quantization, and batching.
• Stay current with the rapidly evolving LLM ecosystem and proactively propose improvements and new approaches.

Required Qualifications

Experience

• 2–4 years of professional software engineering experience, with at least 1–2 years focused on LLM/AI systems.
• Proven experience shipping LLM-based products or agentic AI systems into production environments.

Technical Skills

• Strong proficiency in Python and familiarity with async programming patterns for AI pipelines.
• Hands-on experience with LLM APIs: OpenAI (GPT-4o), Anthropic (Claude), Google (Gemini), or open-source models (Llama, Mistral).
• Experience with agentic frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar.
• Solid understanding of RAG architectures, embedding models, and semantic search.
• Experience with vector databases and similarity search infrastructure.
• Knowledge of REST APIs, microservices architecture, and containerization (Docker/Kubernetes).

Problem-Solving & Mindset

• Strong ability to decompose ambiguous, open-ended problems into structured AI system designs.
• Experience with prompt debugging, LLM evaluation, and iterative refinement workflows.
• Ability to balance research exploration with engineering pragmatism to ship reliable systems.

Preferred Qualifications

• Experience with multi-agent orchestration and agent memory systems (short-term and long-term).
• Familiarity with fine-tuning or RLHF workflows for domain adaptation.
• Background in NLP, information retrieval, or conversational AI.
• Prior experience in B2B SaaS or CRM domain is a plus.
• Contributions to open-source AI/ML projects or published research/blogs.
• Experience with cloud platforms: AWS, GCP, or Azure — particularly AI/ML services.