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Senior Applied AI Engineer at Weekday AI. This role is for one of Weekday’s clients. Min Experience: 8 years. Location: Remote (India). JobType: full-time. What You'll Be Working On. AI Assistant & Agent Systems. . . Agent Architecture & Implementation. : Build sophisticated multi-agent systems that can reason, plan, and execute complex sales workflows. . . Context Management. : Develop systems that maintain conversational context across complex multi-turn interactions. . . LLM and Agentic Platforms. : Build scalable large language model and agentic platforms that enable widespread adoption and viability of agent development within the Apollo ecosystem. . . Backend Systems:. Build back-end systems necessary to support the agents.. . AI features: Conversational AI, Natural Language Search, Personalized Email Generation and similar AI features. . Classical AI/ML (Optional Focus). . . Search Scoring & Ranking. : Develop and improve recommendation systems and search relevance algorithms. . . Entity Extraction. : Build models for automatic company keywords, people keywords, and industry classification. . . Lookalike & Recommendation Systems. : Create intelligent matching and suggestion engines. . . Key Responsibilities. . . Design and Deploy Production LLM Systems. : Build scalable, reliable AI systems that serve millions of users with high availability and performance requirements. . . Agent Development. : Create sophisticated AI agents that can chain multiple LLM calls, integrate with external APIs, and maintain state across complex workflows. . . Prompt Engineering Excellence. : Develop and optimize prompting strategies, understand trade-offs between prompt engineering vs fine-tuning, and implement advanced prompting techniques. . . System Integration. : Build robust APIs and integrate AI capabilities with existing Apollo infrastructure and external services. . . Evaluation & Quality Assurance. : Implement comprehensive evaluation frameworks, devising A/B experiments, and monitoring systems to ensure AI systems meet accuracy, safety, and reliability standards. . . Performance Optimization. : Optimize for cost, latency, and scalability across different LLM providers and deployment scenarios. . . Cross-functional Collaboration. : Work closely with product teams, backend engineers, and stakeholders to translate business requirements into technical AI solutions. . . Required Qualifications. Core AI/LLM Experience (Must-Have). . . 8+ years of software engineering experience. with a focus on production systems. . . 1.5+ years of hands-on LLM experience. (2023-present) building real applications with GPT, Claude, Llama, or other modern LLMs. . . Production LLM Applications. : Demonstrated experience building customer-facing, scalable LLM-powered products with real user usage (not just POCs or internal tools). . . Agent Development. : Experience building multi-step AI agents, LLM chaining, and complex workflow automation. . . Prompt Engineering Expertise. : Deep understanding of prompting strategies, few-shot learning, chain-of-thought reasoning, and prompt optimization techniques. . Technical Engineering Skills. . . Python Proficiency. : Expert-level Python skills for production AI systems. . . Backend Engineering. : Strong experience building scalable backend systems, APIs, and distributed architectures. . . LangChain or Similar Frameworks. : Experience with LangChain, LlamaIndex, or other LLM application frameworks. . . API Integration. : Proven ability to integrate multiple APIs and services to create advanced AI capabilities. . . Production Deployment. : Experience deploying and managing AI models in cloud environments (AWS, GCP, Azure). . Quality & Evaluation Focus. . . Testing & Evaluation. : Experience implementing rigorous evaluation frameworks for LLM systems including accuracy, safety, and performance metrics. . . A/B Testing. : Understanding of experimental design for AI system optimization. . . Monitoring & Reliability. : Experience with production monitoring, alerting, and debugging complex AI systems. . . Data Pipeline Management. : Experience building and maintaining scalable data pipelines that power AI systems. . . What Makes a Great Candidate. Production-First Mindset. . You've built AI systems that real users depend on, not just demos or research projects. . You understand the difference between a working prototype and a production-ready system. . You have experience with user feedback, iterative improvements, and feedback systems. . Technical Depth with Business Impact. . You can design end-to-end systems, including back-end systems, asynchronous workflows, LLMs, and agentic systems. . You understand the cost-benefit trade-offs of different AI approaches. . You've made decisions about when to use different LLM providers, fine-tuning vs prompting, and architecture choices. . Evaluation & Quality Excellence. . You implement repeatable, quantifiable evaluation methodologies. . You track performance across iterations and can explain what makes systems successful. . You prioritize safety, reliability, and user experience alongside capability. . Adaptability & Learning. . You stay current with the rapidly evolving LLM landscape. . You can quickly adapt to new models, frameworks, and techniques. . You're comfortable working in ambiguous problem spaces and breaking down complex challenges. . Company Location: India.