{"id":1332336,"url":"https://alion.io/job/ecolab-principal-ai-engineer-ml-mlops-platform-architect","title":"Principal AI Engineer — ML / MLOps Platform Architect","company":{"id":1754712,"name":"Ecolab","domain":"ecolab.com","url":"https://alion.io/company/ecolab-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":120,"ghost_share":0,"stale_share":0.925,"repost_share":0,"time_to_fill_p50_days":26,"computed_at":"2026-10-22T06:10:10Z"}},"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":29000,"max_usd":77000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":44},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AgentOps","optional":false},{"name":"AI Agents","optional":false},{"name":"Anthropic","optional":false},{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FinOps","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"OpenAI","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-09-21T00:00:00Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-10-22T14:14:29Z","board_verified":true,"closed_at":null,"days_open":31,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":31},"description":"Job Description - Principal Machine Learning Engineer (LLM, Agentic AI & Model Platform)\nPosition Summary\nThe Principal Machine Learning Engineer is a senior technical leader responsible for architecting, building, and operationalizing enterprise-scale AI, Generative AI, and Agentic AI capabilities across Databricks, Azure AI Foundry, and cloud-native AI platforms. This role will lead the strategy, architecture, and implementation of foundation models, custom models, AI platform services, ModelOps, and Agentic AI frameworks that power enterprise AI solutions.\nThe ideal candidate combines deep machine learning expertise with hands-on software engineering, cloud architecture, MLOps, and platform engineering skills. They will drive model lifecycle management, AI gateway architecture, model routing strategies, token optimization, and enterprise AI governance while enabling secure, scalable, and cost-efficient AI adoption across the organization.\nKey Responsibilities\nEnterprise LLM Platform Leadership\nOwn the enterprise strategy for foundation models, frontier models, and custom enterprise models.\nEvaluate, benchmark, onboard, and operationalize leading AI models from OpenAI, Anthropic, Google, Azure AI Foundry, Databricks Mosaic AI, and open-source ecosystems.\nDefine model selection and deployment strategies based on performance, cost, security, latency, and business requirements.\nEstablish enterprise standards for model consumption and governance.\nModel Lifecycle Management (ModelOps)\nArchitect and implement end-to-end model lifecycle management capabilities.\nLead model training, fine-tuning, evaluation, testing, deployment, monitoring, optimization, and retirement processes.\nBuild automated ModelOps and MLOps pipelines to support enterprise-scale AI workloads.\nImplement model versioning, lineage, experimentation tracking, model monitoring, and drift detection frameworks.\nEnsure reproducibility, compliance, governance, and auditability of AI models.\nAgentic AI Architecture\nDesign and implement enterprise Agentic AI architectures and frameworks.\nDevelop multi-agent orchestration patterns, planning frameworks, tool integration, reasoning workflows, memory management, and contextual intelligence capabilities.\nDefine AgentOps standards for deployment, monitoring, evaluation, and governance of autonomous agents.\nEstablish reusable enterprise frameworks supporting scalable agent development and deployment.\nAI Gateway & Model Routing\nArchitect enterprise AI Gateway capabilities for secure model access and governance.\nDesign LLM routing frameworks that dynamically select optimal models based on workload, cost, latency, and performance requirements.\nDefine model consumption patterns for applications, APIs, copilots, and intelligent agents.\nEnable centralized access, governance, monitoring, and observability across all AI services.\nDevelop abstraction layers supporting seamless integration of multiple foundation models.\nToken Optimization & AI FinOps\nDefine token optimization strategies to improve AI cost efficiency and performance.\nImplement prompt engineering, caching, model tiering, response optimization, and intelligent routing techniques.\nEstablish monitoring and reporting frameworks for model utilization, token consumption, and AI infrastructure costs.\nDrive AI FinOps initiatives and platform optimization strategies.\nAI Platform Engineering\nBuild and scale AI platform capabilities on Databricks, Azure AI Foundry, and Azure cloud platforms.\nArchitect enterprise-ready model serving, inference, vector search, RAG, and semantic retrieval solutions.\nDevelop reusable AI platform services, accelerators, SDKs, and reference architectures.\nEnable secure and governed AI consumption across multiple business domains.\nCloud Infrastructure & Security\nDesign cloud-native infrastructure for model training, fine-tuning, and large-scale inference workloads.\nBuild GPU-enabled, highly scalable, resilient, and secure AI environments.\nImplement Infrastructure as Code, automated deployments, and platform observability.\nEstablish security-by-design principles for AI workloads including model security, access controls, secrets management, and responsible AI controls.\nCollaborate with Security and Governance teams to ensure compliance with enterprise standards.\nTechnical Leadership\nServe as the principal technical authority for Machine Learning, LLMs, Agentic AI, and Model Platforms.\nMentor AI Engineers, ML Engineers, Platform Engineers, and Architects.\nLead architecture reviews, platform strategy discussions, and technology evaluations.\nDrive innovation through proof-of-concepts and adoption of emerging AI technologies.\nPartner with business and technology leaders to accelerate AI transformation initiatives.\nRequired Technical Skills\nQualifications\nBachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related field.\n12+ years of experience in Software Engineering, Machine Learning, AI Platform Engineering, or Cloud Architecture.\n5+ years of experience leading enterprise AI/ML platform implementations.\nProven experience deploying and operating large-scale AI, LLM, and Agentic AI solutions in production environments.\nSuccess Metrics\nSuccessful operationalization of frontier and custom foundation models.\nAI platform scalability, reliability, security, and governance compliance.\nReduced model deployment timelines through automated ModelOps capabilities.\nOptimized AI infrastructure and token consumption costs.\nAdoption of reusable AI platform patterns across business domains.\nIncreased speed, quality, and business impact of AI 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