{"id":3776530,"url":"https://alion.io/job/leadsquared-lead-ai-engineer","title":"Lead AI Engineer","company":{"id":59425,"name":"LeadSquared","domain":"leadsquared.com","url":"https://alion.io/company/leadsquared","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Chroma","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Llama","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Mistral","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Weaviate","optional":false},{"name":"Fine-tuning","optional":true},{"name":"NLP","optional":true},{"name":"PostgreSQL","optional":true},{"name":"RLHF","optional":true}],"status":"live","first_seen_at":"2026-11-04T17:21:11Z","employer_posted_date":null,"last_verified_at":"2026-11-04T17:21:11Z","board_verified":false,"closed_at":null,"days_open":1,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":1},"description":"Lead AI Engineer – LLM & Agentic AI Systems\nLocation: Bengaluru\nExperience: 4–6 Years\nDepartment: Engineering – AI/ML\nEmployment: Full-Time\nAbout LeadSquared\nLeadSquared 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.\nOur 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.\nRole Overview\nWe are looking for a hands-on Lead AI Engineer with strong experience in building and deploying LLM-powered applications and Agentic AI systems. The role involves designing scalable AI architectures, developing autonomous agents, and driving end-to-end implementation of intelligent workflows.\nThe ideal candidate should have strong backend engineering skills, expertise in Python, and practical experience with LLMs, RAG pipelines, multi-agent orchestration and production-grade AI applications. The candidate will also contribute to technical decisions, mentor engineers and drive engineering best practices.\nKey Responsibilities\nDesign, develop and deploy LLM-powered agentic systems using frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen or CrewAI.\nBuild and maintain Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma and pgvector.\nDevelop multi-agent orchestration systems with tool calling, function calling and integrations with internal APIs, databases and third-party services.\nDrive prompt engineering, structured output generation and context management to improve AI reliability and performance.\nDesign evaluation frameworks and observability pipelines to monitor agent performance, accuracy, latency and cost.\nLead backend development using Python, building scalable APIs, microservices and asynchronous AI pipelines.\nOptimise LLM inference for latency and cost through caching, batching, quantisation and model selection.\nWork closely with Product and Engineering teams to translate business requirements into scalable AI solutions.\nTake ownership of technical architecture, code quality, production deployments and system performance.\nMentor engineers, conduct code reviews and establish engineering best practices.\nRequired Qualifications\nExperience\n4–6 years of overall software engineering experience.\nStrong hands-on experience building and deploying LLM-based applications and Agentic AI systems.\nProven experience taking AI solutions from development to production.\nTechnical Skills\nStrong proficiency in Python and backend development.\nHands-on experience with LLM APIs such as OpenAI, Anthropic, Gemini or open-source models like Llama and Mistral.\nExperience with agentic frameworks such as LangChain, LangGraph, LlamaIndex, AutoGen or CrewAI.\nStrong understanding of RAG architectures, embeddings, semantic search and vector databases.\nExperience building multi-agent systems, tool calling and function calling.\nStrong understanding of REST APIs, microservices, asynchronous programming and system design.\nExperience with Docker, Kubernetes and cloud platforms such as AWS, GCP or Azure.\nProblem-Solving & Engineering Mindset\nStrong problem-solving and architectural design capabilities.\nAbility to independently drive technical initiatives and solve complex engineering challenges.\nExperience with LLM evaluation, prompt debugging and performance optimisation.\nStrong ownership mindset with the ability to guide and mentor engineering teams.\nPreferred Qualifications\nExperience with multi-agent orchestration and agent memory systems.\nFamiliarity with fine-tuning, model adaptation or RLHF workflows.\nBackground in NLP, information retrieval or conversational AI.\nPrior experience in B2B SaaS or CRM domains.\nExperience with AI/ML cloud services and production-scale AI deployments.\nContributions to open-source AI/ML projects or published technical research.\nWhat We Are Looking For\nA hands-on AI engineering professional who can drive architecture and development, contribute through coding, solve complex AI challenges and take ownership of building scalable, production-ready Agentic AI solutions.","description_format":"text","description_chars":4419,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["CRM Software","Sales Enablement","Marketing Automation"],"lifecycle":[{"event":"open","at":"2026-11-04T23:09:45Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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