{"id":4028442,"url":"https://alion.io/job/cybage-software-technical-ai-architect","title":"Technical AI Architect","company":{"id":59343,"name":"Cybage Software","domain":"cybage.com","url":"https://alion.io/company/cybage-software","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":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"ElasticSearch","optional":false},{"name":"Function Calling","optional":false},{"name":"Gemini","optional":false},{"name":"Git","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"NLP","optional":false},{"name":"OpenAI","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false}],"status":"live","first_seen_at":"2026-11-10T17:35:30Z","employer_posted_date":null,"last_verified_at":"2026-11-10T17:35:30Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"We are looking for a senior engineer with 10+ years of experience into own AI systems in production and set the technical direction for our Generative AI work. You will architect and optimize RAG pipelines, agentic workflows, and enterprise AI applications; establish evaluation and reliability practices; drive latency, cost, and observability improvements; and mentor other engineers. You will partner with product and business stakeholders to shape the roadmap and deliver scalable, secure, enterprise-ready AI solutions. This role is for someone who has taken AI systems to production and is ready to lead.\n\nTechnical Skills:\nProficiency in Python, including asynchronous programming, concurrency, testing, packaging, and performance optimization.\nExtensive experience designing, developing, deploying, and supporting AI-powered applications using Large Language Models (LLMs).\nExperience integrating multiple AI provider APIs such as OpenAI, Anthropic, Gemini, or similar LLM platforms, including model selection, fallback strategies, and provider abstraction.\nStrong experience implementing structured outputs, function/tool calling, streaming responses, context management, and memory strategies for production AI systems.\nExperience designing and optimizing Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embedding models, vector database selection, semantic search, reranking, and retrieval quality optimization.\nStrong understanding of agentic AI workflows, planning, tool integration, multi-agent systems, and business process automation.\nExperience designing AI evaluation strategies using prompt evaluation, groundedness assessment, hallucination reduction, regression testing, and response quality measurement.\nStrong understanding of machine learning, Natural Language Processing (NLP), model evaluation, and practical data engineering concepts.\nExperience working with large-scale structured and unstructured enterprise datasets, document intelligence, and knowledge management systems.\nExperience designing scalable backend architectures, REST APIs, microservices, and distributed AI applications.\nStrong experience with databases such as MongoDB, PostgreSQL, Elasticsearch, vector databases, or similar technologies, including performance tuning and indexing strategies.\nExperience with Git, collaborative software development workflows.\nKnowledge of AI application observability, latency optimization, token optimization, cost optimization, monitoring, and production reliability.\nUnderstanding of AI security concepts, including prompt injection prevention, secure AI application development, responsible AI practices, and secure handling of enterprise data.\nExperience providing technical leadership through architecture reviews, mentoring, code reviews, and engineering best practices.\n\nKey Responsibilities:\nDesign, develop, and own scalable AI-powered applications deployed in production environments.\nArchitect and optimize Retrieval-Augmented Generation (RAG) pipelines, document intelligence platforms, and enterprise AI solutions.\nDesign and implement advanced agentic AI workflows for complex business process automation and multi-step reasoning.\nDefine technical strategies for prompt engineering, structured outputs, context management, and tool integration across AI applications.\nEstablish evaluation frameworks to continuously improve groundedness, response quality, hallucination reduction, and overall AI system reliability.\nOptimize AI applications for latency, scalability, observability, token utilization, operational cost, and production performance.\nDesign scalable backend architectures, REST APIs, microservices, and data pipelines supporting enterprise AI applications.\nMentor engineers through technical guidance, architecture discussions, design reviews, and code reviews.\nCollaborate with engineering, product, and business stakeholders to define technical roadmaps and deliver enterprise AI solutions.\nLead production troubleshooting, reliability improvements, and continuous optimization of AI systems.\nDocument solution architectures, technical designs, implementation guidelines, and operational best practices to support long-term maintainability.\n\nQualifications:\nEducation: Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.\nExperience: 3+ years of professional software/backend engineering experience, including hands-on production Generative AI work.\n\nSoft Skills:\nTechnical leadership sets direction and brings others along, without needing formal authority.\nMentors and grows engineers through guidance, design discussions, and reviews.\nSound judgment and decision-making under ambiguity and competing constraints (quality, cost, speed).\nCommunicates effectively with both technical teams and non-technical business stakeholders.\nTakes ownership of outcomes, including production incidents and their resolution.\nDrives clarity, turns vague problems into concrete and well-scoped technical plans.\nChampions engineering standards, documentation, and long-term maintainability.\nStays current with a fast-moving field and helps the team adopt what matters.\nSkills\nAI, LLM, Machine Learning, NLP, Python, RAG, Retrieval-Augmented 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