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Location
In office
Seniority
Middle · 4+ years exp
Overview
Company
Impact
Profile match
Zensar Technologies is an Indian technology services company founded in 1991 and part of the RPG Group, providing digital engineering, application services and infrastructure management to clients in banking, insurance, retail, manufacturing and healthcare. Its work covers experience design, cloud and data engineering, application development and maintenance, and increasingly applied artificial intelligence, delivered from centres in India, South Africa, Europe and the United States. Headquartered in Pune and listed in India, it earns a large share of revenue from North America and has been repositioning toward higher-value engineering work.

JobSummary

WeareseekinganinnovativeandhighlyskilledAI Engineer to design, develop, deploy, and optimize Artificial Intelligence and Generative AI solutions. The ideal candidate will have expertise in Machine Learning, Large Language Models (LLMs), NaturalLanguageProcessing(NLP),AIapplicationdevelopment,andcloud-basedAIplatforms.Theroleinvolvesbuildingintelligentsystemsthatdriveautomation,enhancedecision-making,anddeliverbusinessvalue.

KeyResponsibilities

  • Design,develop,anddeployAI/MLandGenerativeAIsolutions.
  • Buildandoptimizemachinelearningmodelsforbusinessusecases.
  • DevelopAI-poweredapplicationsusingLLMsandNLPtechniques.
  • ImplementRetrieval-AugmentedGeneration(RAG)frameworksandAIagents.
  • Fine-tuneandevaluatefoundationmodelsforspecificbusinessrequirements.
  • IntegrateAIserviceswithenterpriseapplicationsandAPIs.
  • Developdatapipelinesformodeltraining,validation,anddeployment.
  • Monitormodelperformance,accuracy,andreliabilityinproductionenvironments.
  • CollaboratewithDataScientists,Architects,ProductOwners,andEngineeringteams.
  • EnsureAIsolutionscomplywithsecurity,privacy,andresponsibleAIstandards.
  • StayupdatedwithadvancementsinAI,GenAI,AgenticAI,andMLOps.

RequiredTechnicalSkills

ArtificialIntelligence&MachineLearning

  • MachineLearningAlgorithms
  • DeepLearning
  • Supervised&UnsupervisedLearning
  • ModelTrainingandOptimization
  • FeatureEngineering
  • ModelEvaluationandValidation

GenerativeAI

  • LargeLanguageModels(LLMs)
  • OpenAI,AzureOpenAI
  • Gemini,Claude,Llama
  • PromptEngineering
  • Fine-TuningTechniques
  • RAG(Retrieval-AugmentedGeneration)
  • VectorDatabases

AgenticAIFrameworks

  • LangChain
  • LangGraph
  • CrewAI
  • SemanticKernel
  • AIAgents&Multi-AgentSystems

ProgrammingLanguages

  • Python(Mandatory)
  • SQL
  • JavaScript(GoodtoHave)

AI/MLFrameworks

  • TensorFlow
  • PyTorch
  • Scikit-learn
  • HuggingFaceTransformers

Databases

  • PostgreSQL
  • SQLServer
  • MongoDB

VectorDatabases

  • Pinecone
  • Weaviate
  • ChromaDB
  • FAISS
  • AzureAISearch

CloudPlatforms

  • MicrosoftAzure
  • AWS
  • GoogleCloudPlatform(GCP)

MLOps&DevOps

  • MLflow
  • Kubeflow
  • Docker
  • Kubernetes
  • GitHubActions
  • AzureDevOps
  • Jenkins

DataEngineering

  • Pandas
  • NumPy
  • ApacheSpark
  • DataPipelines

RequiredQualifications

  • Bachelor'sorMaster'sdegreeinComputerScience,ArtificialIntelligence,DataScience,orarelatedfield.
  • 4+yearsofexperienceinAI/MLdevelopment.
  • Hands-onexperiencebuildinganddeployingmachinelearningmodels.
  • StrongproficiencyinPythonandAI/MLframeworks.
  • ExperiencewithGenerativeAI,LLMs,andRAGarchitectures.
  • ExperienceworkingwithcloudAIservicesandAPIs.
  • UnderstandingofresponsibleAIandmodelgovernance.
  • Stronganalyticalandproblem-solvingabilities.

PreferredQualifications

  • ExperiencewithAgenticAIandAutonomousAISystems.
  • AzureAIEngineerAssociate,AWSMachineLearning,orGCPAIcertifications.
  • KnowledgeofMLOpsandmodeldeploymentbestpractices.
  • ExperienceinRetail,E-Commerce,Banking,Healthcare,orSupplyChaindomains.
  • FamiliaritywithAIobservabilityandmonitoringtools.

KeyCompetencies

  • ArtificialIntelligenceDevelopment
  • GenerativeAISolutionDesign
  • ProblemSolving
  • AnalyticalThinking
  • InnovationandResearch
  • StakeholderCommunication
  • CollaborationandTeamwork
  • ContinuousLearning

KeyPerformanceIndicators(KPIs)

  • ModelAccuracyandPerformance
  • AISolutionAdoptionRate
  • ResponseQualityofAISystems
  • Time-to-ProductionforAISolutions
  • CostOptimizationofAIWorkloads
  • ModelReliabilityandAvailability
  • BusinessImpactDelivered
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