{"id":1214694,"url":"https://alion.io/job/weekday-ai-architect","title":"AI Architect","company":{"id":7097,"name":"Weekday","domain":"weekday.works","url":"https://alion.io/company/weekday","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"B","score":81,"open_postings":86,"ghost_share":0,"stale_share":0.977,"repost_share":0,"time_to_fill_p50_days":5,"computed_at":"2026-09-26T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":4000000,"max":7000000,"currency":"INR","period":"year","gross":null,"usd_annual":73374},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"CrewAI","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Tool Use","optional":false},{"name":"Agile","optional":true},{"name":"Anthropic","optional":true},{"name":"CI/CD","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Gemini","optional":true},{"name":"Llama","optional":true},{"name":"Mistral","optional":true},{"name":"OpenAI","optional":true},{"name":"Synthetic Data","optional":true}],"status":"live","first_seen_at":"2026-09-25T07:34:27Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-09-27T01:59:47Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"This role is for one of Weekday’s clients\nSalary range: Rs 4000000 - Rs 7000000 (ie INR 40 - 70 LPA)\nMin Experience: 10+ years\nLocation: Hyderabad\nJobType: full-time\nWe are looking for an AI Software Architect / Senior AI Engineer who is a self-starter and thrives on designing and delivering production-grade AI solutions powered by Large Language Models (LLMs). This role requires deep expertise in architecting, building, and operating complex AI systems, including Retrieval-Augmented Generation (RAG), agentic workflows, tool calling, evaluation frameworks, and observability platforms.\nThe ideal candidate combines strong software engineering fundamentals with demonstrated experience delivering real-world AI products. Beyond experimentation, this individual must be capable of designing scalable, reliable, and cost-effective LLM-powered solutions that operate successfully in production environments. They should possess a strong understanding of modern AI architecture patterns, prompt engineering, retrieval systems, agent orchestration, and AI observability.\nRequirements\nKey Responsibilities\nOwn the architecture of AI-powered services and their integration with backend, mobile, and web applications.\nDesign, build, and maintain production-grade LLM applications using modern AI frameworks and orchestration platforms.\nLead technical design reviews and drive architectural decisions across AI services, backend systems, data pipelines, and cloud infrastructure.\nArchitect agentic workflows involving tool use, function calling, multi-agent systems, planning, memory management, and reasoning chains.\nEstablish evaluation frameworks to measure AI quality, reliability, latency, cost, and business impact.\nImplement AI observability and monitoring using platforms such as Langfuse, LangSmith, OpenTelemetry, and related tooling.\nDesign and implement Retrieval-Augmented Generation (RAG) architectures using vector databases, embeddings, document pipelines, and retrieval optimization techniques.\nCollaborate with software engineers, data scientists, product managers, and domain experts to translate business requirements into AI solutions.\nOptimize prompts, retrieval strategies, model selection, and system architecture for accuracy, reliability, performance, and cost efficiency.\nDesign scalable APIs and services to expose AI capabilities across internal and external applications.\nDefine AI engineering standards, best practices, and governance processes across the organization.\nProvide technical leadership and mentorship to engineers working on AI initiatives.\nLeverage cloud platforms such as Google Cloud Platform (GCP) to deploy and scale AI services.\nDocument AI architectures, workflows, evaluation methodologies, and operational procedures.\nQualifications\nMaster's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or related field.\n10+ years of software engineering experience with at least 5 years focused on LLM-based solutions and generative AI systems.\nDemonstrated experience designing and deploying complex production-grade AI applications using Large Language Models.\nExtensive experience with AI orchestration frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, or equivalent technologies.\nHands-on experience implementing AI observability and evaluation frameworks using Langfuse, LangSmith, or similar platforms.\nProven expertise with Retrieval-Augmented Generation (RAG) architectures and vector database technologies.\nStrong understanding of modern LLM architectures, prompting strategies, context management, embeddings, and retrieval techniques.\nStrong Python development experience and software engineering fundamentals.\nExperience designing scalable APIs and cloud-native architectures.\nAbility to evaluate architectural trade-offs involving model performance, latency, reliability, maintainability, and cost.\nExperience deploying AI solutions in production environments using Docker, Kubernetes, and cloud platforms.\nStrong understanding of structured and unstructured data processing pipelines.\nFamiliarity with modern database technologies including PostgreSQL, vector databases, and document stores.\nExcellent communication, leadership, and mentoring skills and ability to collaborate effectively with cross-functional teams.\nPreferred Skills\nExperience building agentic systems involving tool use, planning, memory, and multi-agent orchestration and skills.\nExperience with model evaluation, benchmarking, AI testing frameworks, and automated quality assessment.\nExperience working with multiple commercial and open-source models including OpenAI, Anthropic, Gemini, Llama, and Mistral.\nFamiliarity with fine-tuning, synthetic data generation, and model optimization techniques.\nFamiliarity with developing and training machine learning models.\nExperience supporting AI products in regulated, privacy-sensitive, or high-availability environments.\nExperience integrating AI capabilities into mobile and web applications.\nFamiliarity with modern software delivery practices including DevOps, CI/CD, and Agile development methodologies.\nMust-have skills\nRAG, LLM, GCP\nGood-to-have skills\nPython, 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