Senior Machine Learning Engineer

Location: BangaloreExperience: 3-5 yearsType: Full-time | On-siteStart: ImmediateWhy this role existsMost companies are using LLMs.Very few are building an advantage from them.Right now, LLM cost is our largest margin constraint, and model behavior is still too generic to be defensible.This role exists to:Turn LLM usage into a cost-efficient systemBuild compounding intelligence across accountsCreate a differentiated analysis layer that competitors can’t replicateWhat you’ll doYou will not just build models.You will own the intelligence and cost structure of the platform.1. Drive down LLM cost dramaticallyReduce cost per interaction from ₹40 → ₹2 within 6 monthsImplement:Model tiering (right model for the right task)Caching strategies (semantic + response caching)Batching and async processingPTU / reserved capacity optimizationEnsure performance does not degrade while reducing cost2. Optimize infrastructure spendReduce cloud spend from ₹20L/month → ₹4L/monthWork across infrastructure layers (Azure / compute / inference)Balance:LatencyCostThroughputTreat infra as a first-class optimization problem3. Build the fine-tuning and learning pipelineDesign systems where:Every interaction improves future performanceBuild pipelines for:Fine-tuningFeedback loopsContinuous model improvementEnsure the 5th customer deployment is structurally better than the 1st4. Create a differentiated intelligence layerBuild analysis systems that:Extract signals from interactionsImprove decision-makingDrive outcome improvementsMove beyond responses → insight + action5. Enable new AI-native product categoriesIdentify opportunities where:AI enables workflows that were not previously possibleBuild foundational ML capabilities to unlock those categoriesFocus on creation, not just efficiency6. Commoditize LLM usage internallyAbstract complexity of LLM usage from product teamsBuild internal systems where:Cost is predictablePerformance is consistentMake LLM usage a reliable utility layerWhat success looks likeCost per interaction drops to ₹2 or lowerInfrastructure spend reduces 5x without performance lossModel performance improves with every deploymentPlatform develops a clear intelligence advantageNew AI-native capabilities become possible due to your systemsWho you areYou have 3-5 years of experience in ML / applied AI / systems engineeringYou have worked with:LLMsInference optimizationProduction ML systemsYou think in:SystemsTrade-offs (cost vs latency vs quality)You care about real-world impact, not just model metricsWhat will make you stand outExperience with:LLM optimization (prompting, fine-tuning, distillation)Distributed systems or infra-level optimizationsHigh-scale inference systemsBuilt systems that:Reduced cost significantlyImproved performance over timeStrong understanding of:Caching strategiesModel routingEvaluation frameworksWhy joinYou will directly impact company margins and scalabilityYour work defines whether we have a defensible ML advantageYou will build systems that move from:Generic AI usage → compounding intelligenceWhat this role is notNot research-onlyNot experimentation without production impactNot isolated from product and business outcomesWhat this role isA builder of ML systems at scaleA driver of cost and performance optimizationA creator of long-term competitive advantageOne question to self-evaluateCan you build ML systems that get cheaper, smarter, and more valuable with every interaction?