Senior AI Engineer – Multi-Agent & LLM Systems

Key ResponsibilitiesMulti-Agent Systems ArchitectureDesign and implement multi-agent LLM orchestration frameworksArchitect:Planner–Executor modelsTool-using agentsMemory-enabled agentsHierarchical and collaborative agent systemsDefine inter-agent communication protocolsBuild structured reasoning and orchestration pipelinesOptimize token usage, latency, throughput, and scalabilityEnsure resilience, failover handling, and workflow robustnessLLM Systems & RAG ArchitectureDesign scalable Retrieval-Augmented Generation (RAG) systemsDefine:Embedding strategiesIntelligent chunking frameworksRetrieval optimization methodsHybrid search architecturesImplement prompt engineering, fine-tuning, and instruction tuning strategiesDesign hallucination mitigation and groundedness systemsEstablish prompt versioning and governance standardsOptimize inference cost and model performanceLLM Evaluation & Reliability EngineeringDesign evaluation frameworks for:Hallucination detectionFaithfulness assessmentResponse quality benchmarkingGroundedness scoringImplement automated LLM evaluation pipelinesBuild synthetic dataset generation systemsDesign human-in-the-loop evaluation workflowsMonitor model drift and agent failuresDevelop observability dashboards and reliability monitoring systemsDefine enterprise AI governance standardsMachine Learning & Predictive SystemsLead development of:Classification and regression modelsDeep learning architecturesAnomaly detection systemsKnowledge graph reasoning enginesEstablish experimentation and statistical validation frameworksOptimize model performance and deployment strategiesProduction AI & InfrastructureArchitect enterprise-grade AI deployment infrastructureDefine and manage:MLOps pipelinesLLMOps workflowsMonitoring & observability systemsDeploy AI systems using:AWS / GCP / AzureDocker / KubernetesCI/CD pipelinesEnsure scalability, reliability, and cost optimization for high-volume AI workloadsTechnical Leadership & StrategyServe as architectural authority for AI systemsMentor AI engineers, ML engineers, and data scientistsConduct architecture reviews and technical evaluationsTranslate business challenges into scalable AI frameworksCollaborate with leadership on AI innovation and strategic roadmap planningRequired QualificationsMaster’s or PhD in:Artificial IntelligenceMachine LearningComputer ScienceRelated Technical Field6+ years of experience in AI/ML EngineeringMinimum 3 years leading complex AI initiativesStrong proficiency in PythonProven experience deploying AI systems into production environmentsRequired Technical SkillsAI/ML ExpertiseMachine Learning AlgorithmsDeep Learning ArchitecturesTransformer ModelsStatistical ModelingReinforcement Learning ConceptsAI Evaluation SystemsMulti-Agent & LLM SystemsLangGraph (Mandatory)LangChainMulti-Agent OrchestrationAgent Workflows & Memory SystemsPrompt Engineering & Fine-TuningRAG System DesignInfrastructure & DeploymentAWS / GCP / AzureDocker & KubernetesCI/CD PipelinesMonitoring & Observability ToolsDistributed Systems ArchitectureDatabases & AI SystemsVector Databases:PineconeWeaviateSimilar PlatformsKnowledge Graph SystemsSearch & Retrieval ArchitecturesPreferred QualificationsExperience building production-grade multi-agent AI systemsExperience scaling enterprise AI platformsExposure to Speech AI systems (STT/TTS)Knowledge graph reasoning expertiseExperience in enterprise AI governance and reliability engineeringWhat We’re Looking ForStrong systems-level architectural mindsetResearch-oriented thinking with production pragmatismExcellent analytical and problem-solving abilitiesStrong mathematical and statistical foundationHigh ownership and execution mindsetExcellent communication and stakeholder management skillsSuccess MetricsSuccessful deployment of multi-agent AI systems in productionReduced hallucination rates and improved AI reliabilityScalable and cost-efficient AI infrastructureInstitutionalized LLM evaluation frameworksMeasurable business impact from AI initiativesWork EnvironmentHigh-impact AI innovation environmentOpportunity to work on enterprise-scale AI systemsCollaborative engineering and research cultureFast-paced and technically challenging projects