[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"seo-content-skill-a2a-global":3,"job-search-\u002Fde-global\u002Fjobs-by-skill\u002Fa2a":4,"skill-page-filters-\u002Fde-global\u002Fjobs-by-skill\u002Fa2a":375,"featured-jobs-\u002Fde-global\u002Fjobs-by-skill\u002Fa2a":518},{},{"total":5,"jobs":6},8,[7,100,168,196,232,259,320,355],{"id":8,"slug":9,"title":10,"raw_title":11,"is_featured":12,"company":13,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":16,"processed_country_iso_code_gmaps":18,"processed_home_office":20,"processed_salary_min":21,"processed_salary_max":22,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":25,"processed_employment_types":27,"processed_it_skills":28,"processed_it_skills_labels":31,"processed_job_expertise_skills":73,"processed_job_expertise_skills_labels":74,"max_cpc":99,"actual_cpc":99},78821,"ind-senior-engineer-data-hartford-global-services-private-limited","IND Senior Engineer, Data",null,false,{"name":14,"slug":15,"logo_url":11},"Hartford Global Services Private Limited","hartford-global-services-private-limited",[17],"Hyderabad",[19],"IN","hybrid",2500000,4000000,"INR","estimated",[26],"FULL_TIME","permanent",{"a2a":29,"aws":29,"mcp":29,"rdf":29,"sql":29,"azure":29,"ci\u002Fcd":29,"neo4j":29,"nosql":29,"spark":29,"aws s3":29,"cypher":29,"docker":29,"python":29,"sparql":29,"aws ec2":29,"mongodb":29,"pytorch":30,"big data":29,"dynamodb":29,"data lake":29,"data mesh":29,"langchain":29,"langgraph":29,"snowflake":29,"aws lambda":29,"kubernetes":29,"tensorflow":30,"aws kinesis":29,"data fabric":29,"apache kafka":29,"document dbs":29,"etl\u002Felt tools":29,"gcp vertex ai":29,"spanner graph":29,"amazon neptune":29,"aws opensearch":29,"graph databases":29,"spark streaming":29,"vector databases":29,"google cloud platform":29},4,3,[32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72],"Apache Kafka","AWS Kinesis","Spark Streaming","SQL","NoSQL","Snowflake","ETL\u002FELT tools","CI\u002FCD","Big Data","AWS","Google Cloud Platform","Azure","Python","Spark","Data Mesh","Data Lake","Data Fabric","Docker","Kubernetes","PyTorch","TensorFlow","Vector Databases","Graph Databases","Document DBs","AWS OpenSearch","GCP Vertex AI","Neo4j","Spanner Graph","Amazon Neptune","MongoDB","DynamoDB","AWS Lambda","AWS S3","AWS EC2","LangChain","LangGraph","MCP","A2A","Cypher","SPARQL","RDF",{"devops":29,"data lakes":29,"data domains":29,"data lineage":29,"data quality":29,"scaled agile":29,"data products":29,"generative ai":29,"data catalogue":29,"data governance":29,"cloud technology":29,"data engineering":29,"data integration":29,"data warehousing":29,"knowledge graphs":29,"data architecture":29,"semantic modeling":29,"prompt engineering":29,"business intelligence":29,"reliability engineering":29,"synthetic data generation":29,"ai-driven data engineering":29,"insurance domain knowledge":30,"retrieval-augmented generation (rag)":29},[75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],"Data Engineering","Data Architecture","Data Warehousing","Data Integration","Data Lakes","Data Domains","Data Products","Business Intelligence","Cloud Technology","Generative AI","Retrieval-Augmented Generation (RAG)","Prompt Engineering","Semantic Modeling","Knowledge Graphs","Synthetic Data Generation","Data Governance","Data Quality","Data Lineage","Data Catalogue","AI-driven Data Engineering","DevOps","Reliability Engineering","Scaled Agile","Insurance Domain Knowledge",1,{"id":101,"slug":102,"title":103,"raw_title":11,"is_featured":12,"company":104,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":105,"processed_country_iso_code_gmaps":106,"processed_home_office":107,"processed_salary_min":108,"processed_salary_max":109,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":110,"processed_employment_types":27,"processed_it_skills":111,"processed_it_skills_labels":113,"processed_job_expertise_skills":134,"processed_job_expertise_skills_labels":135,"max_cpc":99,"actual_cpc":99},78804,"ind-lead-software-engineer-hartford-global-services-private-limited-4","IND Lead Software Engineer",{"name":14,"slug":15,"logo_url":11},[17],[19],"onsite",2000000,3500000,[26],{"a2a":30,"adk":30,"mcp":30,"nexus":112,"numpy":30,"crewai":30,"github":30,"pandas":30,"python":29,"airflow":112,"asyncio":30,"fastapi":30,"pytorch":30,"mcp-scan":112,"pydantic":30,"checkmarx":112,"langchain":30,"langgraph":30,"snowflake":112,"sonarqube":112,"terraform":30,"aws bedrock":30,"hugging face":30,"aws sagemaker":30,"opentelemetry":30,"apache iceberg":112,"google vertex ai":30},2,[44,51,114,115,116,117,118,119,120,121,122,123,124,125,37,126,127,128,129,130,131,66,67,132,133,69,68],"Hugging Face","Pandas","NumPy","AWS SageMaker","AWS Bedrock","Google Vertex AI","Terraform","GitHub","Nexus","SonarQube","Checkmarx","mcp-scan","Airflow","Apache Iceberg","OpenTelemetry","FastAPI","Pydantic","asyncio","CrewAI","ADK",{"hyde":30,"safe":30,"ci\u002Fcd":30,"mlops":29,"devops":30,"raptor":30,"pub\u002Fsub":30,"graphrag":30,"devsecops":30,"agentic ai":29,"agentic rag":29,"rag systems":29,"generative ai":29,"microservices":30,"observability":30,"data pipelines":112,"ml engineering":29,"api development":30,"bias mitigation":112,"drift monitoring":30,"llm architecture":29,"model evaluation":30,"automated testing":112,"adaptive prompting":30,"canary deployments":112,"agile methodologies":30,"context engineering":30,"distributed systems":30,"rollback strategies":112,"counterfactual testing":112,"infrastructure as code":30,"multi-agent coordination":30,"disparate impact analysis":112,"event-driven architecture":30,"point-to-point architecture":30},[136,137,138,84,139,140,141,142,143,144,145,146,147,95,39,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167],"ML Engineering","MLOps","LLM Architecture","Agentic AI","RAG Systems","Agentic RAG","Context Engineering","Adaptive Prompting","Multi-agent Coordination","HyDE","RAPTOR","GraphRAG","Infrastructure as Code","DevSecOps","Agile Methodologies","SAFe","Distributed Systems","Event-driven Architecture","Pub\u002FSub","Point-to-point Architecture","API Development","Microservices","Model Evaluation","Observability","Drift Monitoring","Automated Testing","Canary Deployments","Rollback Strategies","Data Pipelines","Bias Mitigation","Disparate Impact Analysis","Counterfactual Testing",{"id":169,"slug":170,"title":171,"raw_title":11,"is_featured":12,"company":172,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":173,"processed_country_iso_code_gmaps":174,"processed_home_office":20,"processed_salary_min":175,"processed_salary_max":176,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":177,"processed_employment_types":27,"processed_it_skills":178,"processed_it_skills_labels":179,"processed_job_expertise_skills":182,"processed_job_expertise_skills_labels":183,"max_cpc":99,"actual_cpc":99},78805,"ind-lead-engineer-data-hartford-global-services-private-limited","IND Lead Engineer, Data",{"name":14,"slug":15,"logo_url":11},[17],[19],3000000,5000000,[26],{"a2a":29,"mcp":29,"rdf":29,"sql":29,"ci\u002Fcd":29,"neo4j":29,"nosql":29,"aws s3":29,"cypher":29,"docker":29,"python":29,"sparql":29,"aws ec2":29,"mongodb":29,"pytorch":30,"big data":29,"dynamodb":29,"langchain":29,"langgraph":29,"snowflake":29,"aws lambda":29,"kubernetes":29,"tensorflow":30,"aws kinesis":29,"apache kafka":29,"apache spark":29,"etl\u002Felt tools":29,"gcp vertex ai":29,"spanner graph":29,"amazon neptune":29,"aws opensearch":29,"graph databases":29,"vector databases":29,"cloud technologies (aws\u002Fgcp\u002Fazure)":29},[44,35,36,37,38,39,40,180,181,32,33,49,50,51,52,53,54,58,60,61,62,63,64,65,66,67,68,69,70,71,72,56,57,59],"Cloud Technologies (AWS\u002FGCP\u002FAzure)","Apache Spark",{"ai\u002Fml":29,"devops":29,"alerting":29,"analytics":29,"data mesh":29,"data lakes":29,"monitoring":29,"redundancy":29,"data fabric":29,"data catalog":29,"data domains":29,"data lineage":29,"data quality":29,"scaled agile":29,"data products":29,"generative ai":29,"data governance":29,"fault tolerance":29,"data engineering":29,"data integration":29,"data warehousing":29,"knowledge graphs":29,"agentic solutions":29,"data architecture":29,"disaster recovery":29,"semantic modeling":29,"prompt engineering":29,"incident management":29,"business intelligence":29,"reliability engineering":29,"real-time data streaming":29,"synthetic data generation":29,"insurance domain knowledge":30,"unstructured data processing":29,"retrieval-augmented generation (rag)":29},[75,76,77,78,79,80,81,82,90,91,92,184,84,85,86,87,88,89,185,97,95,96,186,187,188,189,190,191,46,48,192,193,194,195,98],"Data Catalog","Agentic Solutions","Disaster Recovery","Fault Tolerance","Redundancy","Monitoring","Alerting","Incident Management","Unstructured Data Processing","Real-Time Data Streaming","AI\u002FML","Analytics",{"id":197,"slug":198,"title":199,"raw_title":11,"is_featured":12,"company":200,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":204,"processed_country_iso_code_gmaps":206,"processed_home_office":20,"processed_salary_min":208,"processed_salary_max":209,"processed_salary_currency":210,"processed_salary_source":24,"processed_working_hours":211,"processed_employment_types":27,"processed_it_skills":212,"processed_it_skills_labels":213,"processed_job_expertise_skills":222,"processed_job_expertise_skills_labels":223,"max_cpc":99,"actual_cpc":99},81553,"facharchitekt-mit-fokusgebiet-ai-im-chapter-data-bi-und-sales-management-mwd-allianz-insurance","Facharchitekt mit Fokusgebiet AI im Chapter Data, BI und Sales Management (m\u002Fw\u002Fd)",{"name":201,"slug":202,"logo_url":203},"Allianz Insurance","allianz-insurance","https:\u002F\u002Fcdn.phenompeople.com\u002FCareerConnectResources\u002FAISAIPGB\u002Fimages\u002FHeader-1706868786965.svg",[205],"Unterföhring (bei München)",[207],"DE",80000,110000,"EUR",[26],{"a2a":29,"aws":29,"mcp":29,"sql":29,"python":29,"keycloak":30,"oauth 2.1":30,"snowflake":29,"terraform":29,"databricks":29,"typescript":29,"opentelemetry":30,"ci\u002Fcd pipelines":29,"containerization":29,"infrastructure-as-code":29,"llm providers (aws bedrock, anthropic claude, openai)":29},[44,214,35,37,215,41,120,128,216,217,68,69,218,219,220,221],"TypeScript","Databricks","OAuth 2.1","Keycloak","LLM Providers (AWS Bedrock, Anthropic Claude, OpenAI)","CI\u002FCD Pipelines","Containerization","Infrastructure-as-Code",{"ai governance":30,"cloud operations":29,"data architecture":29,"multi-agent systems":29,"ai agent architecture":29,"agile software development":29,"data warehouse and analytics":29,"insurance industry knowledge":30,"business process optimization":29},[224,225,76,226,227,228,229,230,231],"AI Agent Architecture","Multi-Agent Systems","Agile Software Development","Insurance Industry Knowledge","Business Process Optimization","AI Governance","Cloud Operations","Data Warehouse and Analytics",{"id":233,"slug":234,"title":235,"raw_title":11,"is_featured":12,"company":236,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":239,"processed_country_iso_code_gmaps":241,"processed_home_office":20,"processed_salary_min":243,"processed_salary_max":244,"processed_salary_currency":245,"processed_salary_source":24,"processed_working_hours":246,"processed_employment_types":27,"processed_it_skills":247,"processed_it_skills_labels":248,"processed_job_expertise_skills":251,"processed_job_expertise_skills_labels":252,"max_cpc":99,"actual_cpc":99},79170,"associate-principal-ai-engineer-transamerica","Associate Principal, AI Engineer",{"name":237,"slug":238,"logo_url":11},"Transamerica","transamerica",[240],"Cedar Rapids",[242],"US",207200,259000,"USD",[26],{"a2a":30,"aws":29,"mcp":30,"docker":30,"python":29,"langchain":30,"kubernetes":30,"strands sdk":30,"aws agentcore":30},[44,41,50,49,66,249,69,68,250],"Strands SDK","AWS AgentCore",{"gen ai":29,"agentic ai":29,"ai governance":30,"ai solution design":29,"operational processes":30,"digital transformation":29,"model lifecycle management":29,"software engineering best practices":29},[253,254,139,255,256,229,257,258],"AI Solution Design","Gen AI","Model Lifecycle Management","Software Engineering Best Practices","Operational Processes","Digital Transformation",{"id":260,"slug":261,"title":262,"raw_title":11,"is_featured":12,"company":263,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":266,"processed_country_iso_code_gmaps":267,"processed_home_office":107,"processed_salary_min":269,"processed_salary_max":270,"processed_salary_currency":271,"processed_salary_source":24,"processed_working_hours":272,"processed_employment_types":27,"processed_it_skills":273,"processed_it_skills_labels":274,"processed_job_expertise_skills":304,"processed_job_expertise_skills_labels":305,"max_cpc":99,"actual_cpc":99},62347,"data-scientist-platform-ai-squad-group-data-office-avpvp-ocbc-bank","Data Scientist, Platform AI Squad, Group Data Office (AVP\u002FVP)",{"name":264,"slug":265,"logo_url":11},"OCBC Bank","ocbc-bank",[],[268],"SG",120000,180000,"SGD",[26],{"s3":29,"a2a":29,"aws":29,"ec2":29,"eks":29,"git":29,"iam":29,"mcp":29,"ray":29,"grpc":29,"helm":29,"rest":29,"vllm":29,"ragas":29,"argocd":29,"docker":29,"mlflow":29,"python":29,"triton":29,"grafana":29,"jenkins":29,"deepeval":29,"bitbucket":29,"gitlab ci":29,"langchain":29,"langgraph":29,"terraform":29,"cloudwatch":29,"kubernetes":29,"llamaindex":29,"prometheus":29,"privatelink":29,"tensorrt-llm":29,"amazon bedrock":29,"github actions":29,"amazon sagemaker":29,"aws codepipeline":29,"bedrock guardrails":29},[44,41,49,50,120,275,276,277,66,67,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,68,69,302,303],"Helm","Git","MLflow","LlamaIndex","vLLM","TensorRT-LLM","Triton","Ray","GitHub Actions","GitLab CI","Bitbucket","Jenkins","AWS CodePipeline","ArgoCD","Amazon Bedrock","Amazon SageMaker","EKS","EC2","S3","IAM","CloudWatch","PrivateLink","Prometheus","Grafana","RAGAS","DeepEval","Bedrock Guardrails","REST","gRPC",{"ci\u002Fcd":29,"mlops":29,"devops":29,"microservices":29,"pagedattention":29,"dynamic batching":29,"model versioning":29,"high availability":29,"metadata tracking":29,"cloud architecture":29,"incident resolution":29,"kv cache offloading":29,"speculative decoding":29,"gpu capacity planning":29,"quantization techniques":29,"telemetry configuration":29,"multi-agent orchestration":29,"llm inference optimization":29,"model lifecycle management":29},[306,137,95,307,308,255,309,39,157,310,311,312,313,314,315,316,317,318,319],"LLM Inference Optimization","Cloud Architecture","Multi-agent Orchestration","GPU Capacity Planning","Quantization Techniques","PagedAttention","Dynamic Batching","KV Cache Offloading","Speculative Decoding","Model Versioning","Metadata Tracking","Telemetry Configuration","Incident Resolution","High Availability",{"id":321,"slug":322,"title":323,"raw_title":11,"is_featured":12,"company":324,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":327,"processed_country_iso_code_gmaps":329,"processed_home_office":20,"processed_salary_min":330,"processed_salary_max":331,"processed_salary_currency":245,"processed_salary_source":24,"processed_working_hours":332,"processed_employment_types":27,"processed_it_skills":333,"processed_it_skills_labels":334,"processed_job_expertise_skills":340,"processed_job_expertise_skills_labels":341,"max_cpc":99,"actual_cpc":99},83787,"senior-staff-ml-engineer-government-employees-insurance-company-2","Senior Staff ML Engineer",{"name":325,"slug":326,"logo_url":11},"Government Employees Insurance Company","government-employees-insurance-company",[328],"Palo Alto",[242],150000,300000,[26],{"a2a":29,"aws":29,"mcp":29,"java":29,"azure":29,"python":29,"langgraph":29,"langsmith":29,"kubernetes":29,"rag pipelines":29,"ci\u002Fcd pipelines":29,"prompt engineering":29},[44,335,50,336,43,41,337,67,69,68,338,339],"Java","CI\u002FCD pipelines","LangSmith","RAG pipelines","Prompt engineering",{"llm":29,"inferencing":29,"generative ai":29,"model training":29,"model finetuning":29,"project planning":29,"agentic workflows":29,"cloud environments":29,"evaluation systems":29,"architecture design":29,"resource allocation":29,"technical leadership":29,"ai\u002Fml application development":29,"software development life cycle":29},[342,84,343,344,345,346,347,348,349,350,351,352,353,354],"AI\u002FML application development","LLM","Agentic workflows","Software development life cycle","Cloud environments","Model training","Model finetuning","Inferencing","Evaluation systems","Technical leadership","Architecture design","Project planning","Resource allocation",{"id":356,"slug":357,"title":358,"raw_title":11,"is_featured":12,"company":359,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":360,"processed_country_iso_code_gmaps":361,"processed_home_office":20,"processed_salary_min":175,"processed_salary_max":176,"processed_salary_currency":23,"processed_salary_source":24,"processed_working_hours":362,"processed_employment_types":27,"processed_it_skills":363,"processed_it_skills_labels":364,"processed_job_expertise_skills":368,"processed_job_expertise_skills_labels":369,"max_cpc":99,"actual_cpc":374},51724,"ind-staff-software-engineer-hartford-global-services-private-limited-3","IND Staff Software Engineer",{"name":14,"slug":15,"logo_url":11},[17],[19],[26],{"s3":30,"a2a":29,"adk":29,"emr":30,"mcp":29,"glue":30,"ci\u002Fcd":30,"nexus":30,"numpy":29,"crewai":29,"docker":30,"github":30,"pandas":29,"python":29,"airflow":30,"fastapi":29,"pytorch":29,"mcp-scan":30,"pydantic":29,"redshift":30,"checkmarx":30,"langchain":29,"langgraph":29,"snowflake":30,"sonarqube":30,"terraform":30,"kubernetes":30,"aws bedrock":29,"hugging face":29,"aws sagemaker":29,"opentelemetry":30,"apache iceberg":30,"google vertex ai":29},[44,51,114,115,116,129,130,128,120,121,117,118,119,66,67,132,133,69,68,49,50,39,122,123,124,125,37,126,293,365,366,367,127],"Glue","EMR","Redshift",{"hyde":29,"safe":29,"mlops":29,"devops":29,"raptor":29,"pub\u002Fsub":29,"graphrag":29,"devsecops":29,"agentic ai":29,"agentic rag":29,"rag systems":29,"generative ai":29,"data pipelines":30,"bias mitigation":30,"drift monitoring":29,"llm architecture":29,"machine learning":29,"model evaluation":29,"adaptive prompting":29,"agile methodologies":29,"context engineering":29,"distributed systems":29,"counterfactual testing":30,"infrastructure as code":29,"multi-agent coordination":29,"disparate impact analysis":30,"event-driven architecture":29,"point-to-point architecture":29},[370,137,138,84,139,140,141,142,143,371,145,146,147,158,160,152,372,154,373,95,149,148,150,151,164,165,166,167],"Machine Learning","Multi-Agent Coordination","Event-Driven Architecture","Point-to-Point Architecture",0.5,{"industry":376,"employment_type":387,"expertise_skills":390,"it_skills":450,"salary_currency":511,"processed_working_hours":516},[377,381,384],{"key":378,"label":379,"count":380},"insurance","Insurance",6,{"key":382,"label":383,"count":112},"financial services","Financial Services",{"key":385,"label":386,"count":99},"banking","Banking",[388],{"key":27,"label":389,"count":5},"PERMANENT",[391,395,398,401,403,406,408,411,413,415,417,420,422,424,427,429,431,434,436,438,441,444,446,448],{"key":392,"label":393,"slug":392,"count":394},"devops","Devops",5,{"key":396,"label":397,"slug":396,"count":394},"generative-ai","Generative Ai",{"key":399,"label":400,"slug":399,"count":30},"mlops","Mlops",{"key":402,"label":76,"slug":402,"count":30},"data-architecture",{"key":404,"label":405,"slug":404,"count":30},"agentic-ai","Agentic Ai",{"key":407,"label":166,"slug":407,"count":112},"disparate-impact-analysis",{"key":409,"label":410,"slug":409,"count":112},"retrieval-augmented-generation-rag","Retrieval-Augmented Generation (Rag)",{"key":412,"label":157,"slug":412,"count":112},"microservices",{"key":414,"label":371,"slug":414,"count":112},"multi-agent-coordination",{"key":416,"label":97,"slug":416,"count":112},"scaled-agile",{"key":418,"label":419,"slug":418,"count":112},"point-to-point-architecture","Point-To-Point Architecture",{"key":421,"label":152,"slug":421,"count":112},"distributed-systems",{"key":423,"label":82,"slug":423,"count":112},"business-intelligence",{"key":425,"label":426,"slug":425,"count":112},"infrastructure-as-code","Infrastructure As Code",{"key":428,"label":96,"slug":428,"count":112},"reliability-engineering",{"key":430,"label":92,"slug":430,"count":112},"data-lineage",{"key":432,"label":433,"slug":432,"count":112},"agentic-rag","Agentic Rag",{"key":435,"label":158,"slug":435,"count":112},"model-evaluation",{"key":437,"label":77,"slug":437,"count":112},"data-warehousing",{"key":439,"label":440,"slug":439,"count":112},"graphrag","Graphrag",{"key":442,"label":443,"slug":442,"count":112},"raptor","Raptor",{"key":445,"label":142,"slug":445,"count":112},"context-engineering",{"key":447,"label":79,"slug":447,"count":112},"data-lakes",{"key":449,"label":143,"slug":449,"count":112},"adaptive-prompting",[451,454,456,458,461,463,466,469,471,473,476,478,481,483,486,488,491,493,496,498,501,503,506,509],{"key":452,"label":453,"slug":452,"count":5},"mcp","Mcp",{"key":455,"label":69,"slug":455,"count":5},"a2a",{"key":457,"label":44,"slug":457,"count":5},"python",{"key":459,"label":460,"slug":459,"count":380},"langgraph","Langgraph",{"key":462,"label":50,"slug":462,"count":380},"kubernetes",{"key":464,"label":465,"slug":464,"count":380},"langchain","Langchain",{"key":467,"label":468,"slug":467,"count":394},"aws","Aws",{"key":470,"label":37,"slug":470,"count":394},"snowflake",{"key":472,"label":49,"slug":472,"count":394},"docker",{"key":474,"label":475,"slug":474,"count":29},"pytorch","Pytorch",{"key":477,"label":120,"slug":477,"count":29},"terraform",{"key":479,"label":480,"slug":479,"count":30},"opentelemetry","Opentelemetry",{"key":482,"label":35,"slug":482,"count":30},"sql",{"key":484,"label":485,"slug":484,"count":30},"cicd","Ci\u002FCd",{"key":487,"label":293,"slug":487,"count":112},"s3",{"key":489,"label":490,"slug":489,"count":112},"crewai","Crewai",{"key":492,"label":122,"slug":492,"count":112},"nexus",{"key":494,"label":495,"slug":494,"count":112},"gcp-vertex-ai","Gcp Vertex Ai",{"key":497,"label":114,"slug":497,"count":112},"hugging-face",{"key":499,"label":500,"slug":499,"count":112},"rdf","Rdf",{"key":502,"label":60,"slug":502,"count":112},"amazon-neptune",{"key":504,"label":505,"slug":504,"count":112},"cicd-pipelines","Ci\u002FCd Pipelines",{"key":507,"label":508,"slug":507,"count":112},"neo4j","Neo4J",{"key":510,"label":40,"slug":510,"count":112},"big-data",[512,513,514,515],{"key":23,"label":23,"count":29},{"key":245,"label":245,"count":112},{"key":210,"label":210,"count":99},{"key":271,"label":271,"count":99},[517],{"key":26,"label":26,"count":5},{"total":99,"jobs":519},[520],{"id":521,"slug":522,"title":523,"raw_title":11,"is_featured":524,"company":525,"raw_hiringOrganization_logo_url":11,"processed_city_gmaps":529,"processed_country_iso_code_gmaps":531,"processed_home_office":532,"processed_salary_min":533,"processed_salary_max":534,"processed_salary_currency":210,"processed_salary_source":11,"processed_working_hours":535,"processed_employment_types":389,"processed_it_skills":537,"processed_it_skills_labels":11,"processed_job_expertise_skills":538,"processed_job_expertise_skills_labels":539,"max_cpc":546,"actual_cpc":11},52265,"privatkundenbetreuer-mwd-banking-frankfurt-am-main-white-horse-international-gmbh","Privatkundenbetreuer (m\u002Fw\u002Fd) – Banking | Frankfurt am Main",true,{"name":526,"slug":527,"logo_url":528},"White Horse International GmbH","white-horse-international-gmbh","https:\u002F\u002Fwhitehorse-international.com\u002Fwp-content\u002Fuploads\u002F2022\u002F03\u002Fwhite-horse-logo-final-gif.gif",[530],"Frankfurt am Main",[207],"HYBRID",45000,65000,[536],"full_time",{},{"Financial Analysis":30,"Regulatory Compliance":30,"New Customer Acquisition":30,"Private Banking Advisory":30,"Banking Products Knowledge":30,"Customer Relationship Management":30},[540,541,542,543,544,545],"Private Banking Advisory","Financial Analysis","Banking Products Knowledge","Regulatory Compliance","Customer Relationship Management","New Customer Acquisition",0.8]