{"id":1268737,"url":"https://alion.io/job/nielseniq-genai-engineer-database","title":"GenAI Engineer - Database","company":{"id":9378,"name":"NielsenIQ","domain":"nielseniq.com","url":"https://alion.io/company/nielseniq","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":80,"open_postings":19,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":14,"computed_at":"2026-09-28T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":64000,"period":"year","method":"role_seniority_country_cell","sample_n":10},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Azure AKS","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Bicep","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"FAISS","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Go","optional":false},{"name":"Google GKE","optional":false},{"name":"Grafana","optional":false},{"name":"GraphDB","optional":false},{"name":"Incident Management","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Memgraph","optional":false},{"name":"Neo4j","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"Pinecone","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prefect","optional":false},{"name":"Prometheus","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-09-25T13:20:36Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-09-27T20:07:54Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"NIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights-delivered with advanced analytics through state-of-the-art platforms-NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.\n We are seeking a highly skilled GenAI MLOps Engineer to join our AI Engineering team. In this role, you will design, build, deploy, and operate the core infrastructure powering our Generative AI and Machine Learning solutions. You will collaborate closely with Data Scientists, AI Engineers, Platform Engineers, and Software Development teams to productionize LLM-based applications, automate workflows, optimize infrastructure, and ensure scalable, secure, and cost-effective AI operations.\nThe ideal candidate possesses strong expertise in cloud-native MLOps, model deployment, CI/CD automation, Kubernetes, Infrastructure-as-Code, and modern GenAI orchestration frameworks.\nKey Responsibilities\n1. ML Pipeline Engineering & CI/CD\nDesign, build, and maintain end-to-end ML pipelines covering:Data ingestion\nData preprocessing\nModel training\nEvaluation\nDeployment\nMonitoring\n\nDevelop scalable workflow orchestration using tools such as:Airflow\nPrefect\nAzure ML Pipelines\nSageMaker Pipelines\nVertex AI Pipelines\n\nBuild and maintain automated CI/CD pipelines using:GitHub Actions\nAzure DevOps\nJenkins\n\nAutomate code quality checks, security scanning, testing, model validation, and deployment processes.\n2. Model Deployment & Serving\nContainerize AI/ML workloads using Docker.\nDeploy and manage ML inference workloads on:Kubernetes (AKS/EKS/GKE)\nServerless platforms\nCloud-native AI services\n\nImplement advanced deployment strategies including:Canary deployments\nBlue-Green deployments\nShadow deployments\nA/B testing\n\nSupport deployment of LLMs, RAG systems, and AI agents into production environments.\n3. Monitoring, Observability & Reliability\nImplement observability for AI systems through logs, metrics, and distributed tracing.\nMonitor:Model latency\nThroughput\nCost utilization\nToken consumption\nUser traffic\nService availability\n\nCreate dashboards and alerting frameworks using:Prometheus\nGrafana\nDatadog\nAzure Monitor\nAWS CloudWatch\n\nDetect and resolve:Model drift\nData drift\nPerformance degradation\nInfrastructure incidents\n\n4. Cloud & Infrastructure Engineering\nOperate and optimize AI workloads on at least one major cloud platform:Microsoft Azure\nAWS\nGoogle Cloud Platform\n\nManage AI services such as:Azure Databricks\nAzure OpenAI\nAWS SageMaker\nAmazon Bedrock\nVertex AI\n\nBuild and maintain Infrastructure-as-Code using:Terraform\nCloudFormation\nARM/Bicep Templates\n\nProvision and manage:Compute clusters\nNetworking\nStorage\nSecurity controls\nManaged AI services\n\n5. Generative AI Orchestration & Vector Search\nBuild and maintain GenAI workflows using frameworks such as:LangChain\nLangGraph\nLangfuse\nLlamaIndex\nSemantic Kernel\n\nSupport Retrieval-Augmented Generation (RAG) architectures.\nDevelop and optimize:Embedding pipelines\nVector database integrations\nIndex refresh processes\nKnowledge retrieval systems\n\nWork with vector databases including:Pinecone\nWeaviate\nAzure AI Search\nOpenSearch\nChromaDB\nFAISS\n\n6. Security, Governance & Compliance\nImplement secure AI deployment practices.\nManage secrets and credentials using enterprise-grade security solutions.\nEnsure compliance with organizational security, governance, and data privacy standards.\nApply role-based access control (RBAC), encryption, and audit logging practices.\nSupport Responsible AI and model governance initiatives.\n7. Cost Optimization & Performance Engineering\nMonitor cloud consumption and AI infrastructure costs.\nOptimize:GPU utilization\nCompute efficiency\nModel serving costs\nToken usage\nStorage consumption\n\nRecommend architectural improvements that improve scalability and reduce operational expenses.\n8. Cross-Functional Collaboration\nPartner with Data Scientists and AI Engineers to productionize models.\nCollaborate with Software Engineering teams to integrate AI services into products.\nParticipate in architectural reviews and technical design discussions.\nSupport incident management and operational excellence initiatives.\n9. Documentation & Operational Excellence\nCreate and maintain:Architecture diagrams\nTechnical documentation\nRunbooks\nSOPs\nDeployment guides\nOn-call support documentation\n\nEstablish best practices for AI platform operations and reliability.\n 5+ years of experience in DevOps, Platform Engineering, SRE, or MLOps roles.\nMinimum 3+ years supporting Machine Learning, Deep Learning, or AI production systems.\nProficient in Databases specially Graph Db like Neo4j, memgraph (NosQL and SQL\nMust be able to do Data Modelling \nMust know about Embeddings, Vector Database, Semantic Search\nMust have scripting and automation skills using Python, Golang, Bash, or similar languages.\nStrong hands-on expertise with one major cloud platform (Azure, AWS, or GCP).\nExperience deploying AI/ML workloads at scale.\nStrong experience with:Docker\nKubernetes\nContainer orchestration\n\nProven expertise building CI/CD pipelines.\nHands-on experience with Infrastructure-as-Code tools.\nExperience with monitoring and observability platforms.\nWorking knowledge of:LLMs\nPrompt engineering\nRAG architectures\nVector databases\nGenAI orchestration frameworks\n\nPreferred Qualifications\nExperience working with Azure OpenAI, Amazon Bedrock, or Vertex AI.\nHands-on experience supporting production LLM applications.\nFamiliarity with GPU infrastructure and optimization.\nExperience with model evaluation frameworks and LLM observability tools.\nKnowledge of Responsible AI, AI governance, and security best practices.Relevant cloud certifications (Azure, AWS, or GCP) are a plus.\n\n Our Benefits\nFlexible working environment\nVolunteer time off\nLinkedIn Learning\nEmployee-Assistance-Program (EAP)\nNIQ may utilize artificial intelligence (AI) tools at various stages of the recruitment process, including résumé screening, candidate assessments, interview scheduling, job matching, communication support, and certain administrative tasks that help streamline workflows. These tools are intended to improve efficiency and support fair and consistent evaluation based on job-related criteria. All use of AI is governed by NIQ’s principles of fairness, transparency, human oversight, and inclusion. Final hiring decisions are made exclusively by humans. NIQ regularly reviews its AI tools to help mitigate bias and ensure compliance with applicable laws and regulations. If you have questions, require accommodations, or wish to request human review were permitted by law, please contact your local HR representative. For more information, please visit NIQ’s AI Safety Policies and Guiding Principles: https://nielseniq.com/global/en/info/niqs-ai-safety-policies/\nAbout NIQ\nNIQ is the world’s leading consumer intelligence company, delivering the most complete understanding of consumer buying behavior and revealing new pathways to growth. In 2023, NIQ combined with GfK, bringing together the two industry leaders with unparalleled global reach. With a holistic retail read and the most comprehensive consumer insights-delivered with advanced analytics through state-of-the-art platforms-NIQ delivers the Full View™. NIQ is an Advent International portfolio company with operations in 100+ markets, covering more than 90% of the world’s population.\nFor more information, visit NIQ.com\nWant to keep up with our latest updates?\nFollow us on: LinkedIn | Instagram | Twitter | Facebook\nOur commitment to Diversity, Equity, and Inclusion\nAt NIQ, we are steadfast in our commitment to fostering an inclusive workplace that mirrors the rich diversity of the communities and markets we serve. We believe that embracing a wide range of perspectives drives innovation and excellence. All employment decisions at NIQ are made without regard to race, color, religion, sex (including pregnancy, sexual orientation, or gender identity), national origin, age, disability, genetic information, marital status, veteran status, or any other characteristic protected by applicable laws. We invite individuals who share our dedication to inclusivity and equity to join us in making a meaningful impact. To learn more about our ongoing efforts in diversity and inclusion, please visit the https://nielseniq.com/global/en/news-center/diversity-inclusion","description_format":"text","description_chars":8686,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":["Equity","Flexible schedule"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Commerce","Government"],"lifecycle":[{"event":"open","at":"2026-09-25T23:13:32Z"}],"liveness":{"score":61,"band":"ok","label":"Likely open","p_open":1,"p_active":0.615,"p_room":1,"age_days":2,"expected_fill_days":14,"reasons":["conf:9","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/nielseniq-genai-engineer-database","json_url":"https://alion.io/job/nielseniq-genai-engineer-database.json","meta":{"generated_at":"2026-09-28T06:44:15Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":4838,"day_limit":5000,"remaining_today":162,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}