{"id":3481809,"url":"https://alion.io/job/montycloud-principal-artificial-intelligence-engineer","title":"Principal Artificial Intelligence Engineer","company":{"id":10500,"name":"MontyCloud","domain":"montycloud.com","url":"https://alion.io/company/montycloud","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Keka","truth_index":{"grade":"A","score":88,"open_postings":4,"ghost_share":0,"stale_share":0.5,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-30T05:48:23Z"}},"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":{"min_usd":27000,"max_usd":70000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":47},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"LLMOps","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Anthropic","optional":true},{"name":"AutoGen","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"AWS Strands Agents","optional":true},{"name":"Azure","optional":true},{"name":"CrewAI","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"Machine Learning","optional":true},{"name":"OpenAI","optional":true},{"name":"RAG","optional":true},{"name":"Reranking","optional":true}],"status":"live","first_seen_at":"2026-10-29T17:22:29Z","employer_posted_date":null,"last_verified_at":"2026-10-29T17:22:29Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Role Overview\n\nAs a Principal AI Engineer at MontyCloud, you will define and drive the technical vision for agentic AI systems powering the next generation of intelligent cloud operations. This role focuses on architecting scalable, production-grade AI systems, establishing engineering standards, mentoring senior engineers, and leading strategic technical initiatives across the organization. You will work at the intersection of AI, cloud infrastructure, and autonomous operations to build systems that are reliable, observable, and capable of operating at enterprise scale.\n\nKey Responsibilities\n\nTechnical Leadership & Architecture\nDefine and own the technical vision for agentic AI systems across the platform\nArchitect scalable multi-agent systems, orchestration frameworks, MCP server infrastructure, retrieval and memory pipelines, and observability layers\nDrive architectural decisions related to MCP/ tool ecosystems, AI platform design, and LLMOps infrastructure\nEvaluate emerging AI technologies, frameworks, and models to influence engineering and product roadmaps\nCreate and maintain Architecture Decision Records (ADRs) and technical standards\nEngineering & Delivery\nDesign and develop critical AI platform components and infrastructure\nEstablish AI engineering best practices and discipline across the organisation - design patterns, evaluation practices, prompt engineering, reliability standards, governance, and cost optimization\nLead cross-functional technical initiatives to improve AI system quality, reliability and scalability\nCollaborate with platform, infrastructure, and data engineering teams to embed AI-driven automation into cloud operations workflows\nMentorship & Technical Community\nMentor Lead and Staff AI Engineers through architecture reviews, design discussions, and problem-solving sessions\nConduct rigorous technical reviews of designs, architectures, and major code contributions\nContribute to MontyCloud's technical brand through technical writing, open-source contributions, or speaking engagements\nInnovation & Strategic Impact\nIdentify opportunities where agentic AI can create significant product or operational improvements\nBuild prototypes, technical proposals, and proof-of-concepts to validate new ideas\nStay current with advancements in AI research, agentic frameworks, and LLMOps practices\n\nDesired Skills and Requirements\n\nMust Have\n\nAgentic AI & Multi-Agent Systems\nProduction-grade agentic AI system design and development\nAgentic AI System Design & Architecture - Multi-agent architectures and orchestration, Agent-to-agent communication, Agent memory and planning strategies, Tool integration and MCP server design\nAgent orchestration frameworks - LangGraph, CrewAI, AutoGen, Strands Agents, or equivalent agentic AI frameworks\nLLMOps & AI Platform Engineering\nAI Governance & Lifecycle Management - Prompt versioning and governance, evaluation frameworks, regression detection\nAI Observability & Monitoring - Output quality monitoring, Agent tracing and observability\nAI Cost Management - Cost governance for high-scale AI workloads\nCloud & Infrastructure\nCloud AI Platforms & Services - AWS cloud ecosystem, AWS Bedrock, AgentCore\nCloud-Native Infrastructure & Deployment - Cloud-native AI deployments, Kubernetes, Docker\nInfrastructure as Code (IaC) - Terraform\nFoundation Models & AI Integrations\nFoundation model API integration - OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Hugging Face\nMCP and AI tool integration architecture\nRAG & Knowledge Systems\nRetrieval-Augmented Generation (RAG) and Graph-RAG architectures\nEmbedding strategies, retrieval and reranking systems\nKnowledge graph integrations\nTechnical Leadership and Communication\nCross-team technical influence\nTechnical communication and documentation\nOrganization-level engineering ownership\nProactive problem identification and resolution\n\nGood to Have\n\nDomain Experience\nAI systems for cloud operations and infrastructure automation\nDeveloper tooling platforms\nAI Deployment & Optimization\nServerless AI deployment patterns\nAI inference cost optimization\nAdvanced AI Techniques Exposure\nModel fine-tuning and RLHF\nAdvanced model evaluation techniques\nIndustry & Community Exposure\nAI-first or cloud-native product company\nOpen-source contributions, technical blogs, conference talks, or published research in AI/agentic systems\n\nExperience\n\n12+ years of overall software engineering experience\nPrior experience in a Principal Engineer role or equivalent individual contributor (IC) role\nSignificant recent hands-on experience building and deploying applied AI systems in production environments\nProven track record of leading large-scale technical initiatives across multiple teams or product areas\nDemonstrated expertise in architecting enterprise-scale AI platforms and cloud-native AI workloads\nExperience mentoring senior engineers and influencing technical strategy at an organizational level\n\nEducation\n\nBachelor's or Master's degree in Computer Science / Artificial Intelligence / Machine Learning / Engineering / or any related technical discipline\nEquivalent practical experience in advanced AI system design and distributed cloud platforms may also be 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