{"id":162509,"url":"https://alion.io/job/hp-inc-senior-machine-learning-engineer","title":"Senior Machine Learning Engineer","company":{"id":72,"name":"HP Inc.","domain":"hp.com","url":"https://alion.io/company/hewlett-packard","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":83,"open_postings":165,"ghost_share":0,"stale_share":0.673,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-10-19T05:45:15Z"}},"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":["Bengaluru, India","Tlaquepaque, Mexico"],"countries":["IN","MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Azure DevOps","optional":false},{"name":"Blue-Green Deployment","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Configuration Management","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"IAM","optional":false},{"name":"Incident Management","optional":false},{"name":"Jenkins","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"SRE","optional":false},{"name":"Terraform","optional":false},{"name":"Tool Use","optional":false},{"name":"Amazon CloudWatch","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure","optional":true},{"name":"Datadog","optional":true},{"name":"Grafana","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Milvus","optional":true},{"name":"OpenSearch","optional":true},{"name":"OpenTelemetry","optional":true},{"name":"pgvector","optional":true},{"name":"Pinecone","optional":true},{"name":"PostgreSQL","optional":true},{"name":"Prometheus","optional":true},{"name":"Semantic Kernel","optional":true},{"name":"Splunk","optional":true},{"name":"Weaviate","optional":true}],"status":"live","first_seen_at":"2026-08-24T00:00:00Z","employer_posted_date":"2026-09-01","last_verified_at":"2026-10-19T17:21:50Z","board_verified":true,"closed_at":null,"days_open":56,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":56},"description":"Senior Machine Learning EngineerDescription -\nWe are looking for a Senior MLOps Engineer to design, build, and operate the infrastructure that enables machine learning models and large language models to be deployed safely, reliably, and at scale.\nIn this role, you will create the end-to-end capabilities required to move models from experimentation into production, expose them through secure and highly available endpoints, and enable users and applications to interact with AI-powered services.\nYou will work across AWS and Databricks to establish robust CI/CD pipelines, model-serving infrastructure, observability, governance, rollback mechanisms, and operational standards. You will partner closely with data scientists, machine learning engineers, software engineers, security teams, and platform engineers.\nThe ideal candidate combines strong cloud and DevOps engineering skills with a practical understanding of machine learning systems, LLM deployment patterns, and production reliability.\nKey Responsibilities\nMLOps Platform and Architecture\nDesign and implement a scalable MLOps platform using AWS and Databricks.\n\nDefine reference architectures and reusable deployment patterns for traditional machine learning models, deep learning models, and large language models.\n\nBuild standardized workflows that move models from development and validation into staging and production.\n\nDevelop self-service capabilities that allow data scientists and ML engineers to deploy models without manually managing infrastructure.\n\nEstablish clear separation between development, testing, staging, and production environments.\n\nDesign multi-region or multi-availability-zonearchitectures where required by business continuity and availability objectives.\n\nCI/CD and Model Deployment\nBuild automated CI/CD pipelines for model code, inference services, infrastructure, configuration, and model artifacts.\n\nImplement automated testing across the deployment lifecycle, including:\nUnit testing\n\nIntegration testing\n\nModel validation\n\nData contract validation\n\nAPI and endpoint testing\n\nSecurity testing\n\nPerformance and load testing\n\nRegression testing\n\nAutomate model packaging, containerization, versioning, approval, promotion, and deployment.\n\nSupport deployment strategies such as blue-green deployments, canary releases, shadow deployments, and controlled traffic shifting.\n\nImplement reliable rollback and roll-forward mechanisms for application code, infrastructure, model versions, prompts, and configuration.\n\nEnsure deployments are reproducible, auditable, and recoverable.\n\nModel and LLM Serving\nDesign and operate secure, scalable, low-latency inference endpoints.\n\nDeploy models using appropriate services and patterns across AWS and Databricks, such as:\nDatabricks Model Serving\n\nMLflow Model Registry\n\nAmazon SageMaker\n\nAmazon ECS or EKS\n\nAWS Lambda, where appropriate\n\nAPI Gateway\n\nApplication Load Balancers\n\nBuild synchronous, asynchronous, batch, and streaming inference capabilities.\n\nDesign serving architectures for LLM-powered applications, including:\nHosted foundation models\n\nOpen-source models\n\nFine-tuned models\n\nRetrieval-augmented generation\n\nEmbedding services\n\nVector search\n\nPrompt and response orchestration\n\nTool-calling and agentic workflows\n\nOptimize inference performance, scalability, GPU utilization, concurrency, throughput, latency, and cost.\n\nImplement autoscaling, request throttling, queuing, caching, timeout handling, and graceful degradation.\n\nReliability, Recovery, and Business Continuity\nBuild recoverable model-serving endpoints with clearly defined recovery time and recovery point objectives.\n\nImplement automated health checks, failover mechanisms, retry policies, circuit breakers, and service recovery procedures.\n\nDesign backup and recovery processes for:\nModel artifacts\n\nModel registry metadata\n\nFeature definitions\n\nDeployment configurations\n\nInfrastructure state\n\nPrompts and application configuration\n\nVector indexes and knowledge-base assets\n\nCreate disaster recovery procedures and regularly test restoration and failover capabilities.\n\nEnsure production services can recover from failed deployments, infrastructure outages, model errors, and upstream dependency failures.\n\nDevelop operational runbooks and incident response procedures.\n\nMonitoring and Observability\nImplement end-to-end observability for infrastructure, applications, models, data, and user interactions.\n\nMonitor:\nAvailability\n\nRequest volume\n\nLatency\n\nError rates\n\nResource utilization\n\nModel performance\n\nData quality\n\nData drift\n\nConcept drift\n\nPrediction distributions\n\nLLM response quality\n\nHallucination and safety indicators\n\nToken consumption\n\nCost per request\n\nEstablish dashboards, alerts, service-level indicators, and service-level objectives.\n\nIntegrate monitoring with incident management and on-call processes.\n\nEnable traceability from user requests through model inference, retrieval, orchestration, and downstream services.\n\nSupport root-cause analysis by maintaining structured logs, metrics, traces, model lineage, and deployment history.\n\nSecurity and Governance\nImplement security controls for model-serving environments, APIs, data access, and deployment pipelines.\n\nApply least-privilege access using AWS IAM, Databricks permissions, service principals, and role-based access control.\n\nSecure secrets, credentials, API keys, certificates, and tokens using approved secrets-management solutions.\n\nImplement encryption in transit and at rest.\n\nDesign private networking, endpoint controls, firewall rules, and secure connectivity patterns.\n\nSupport authentication, authorization, rate limiting, and tenant isolation for AI services.\n\nEnsure models and LLM applications comply with organizational requirements for privacy, security, auditability, and responsible AI.\n\nMaintain model lineage, approval records, version history, and deployment audit trails.\n\nImplement controls for sensitive data, personally identifiable information, prompt injection, unsafe outputs, and unauthorized model access.\n\nInfrastructure as Code and Automation\nBuild and maintain cloud infrastructure using Infrastructure as Code tools such as Terraform or AWS CloudFormation.\n\nAutomate environment provisioning, policy enforcement, deployment configuration, and platform upgrades.\n\nCreate reusable modules, templates, libraries, and deployment frameworks.\n\nImplement configuration management and environment-specific parameterization.\n\nEnsure infrastructure changes are peer-reviewed, tested, version-controlled, and traceable.\n\nCollaboration and Engineering Standards\nPartner with data scientists and ML engineers to productionize models and define deployment requirements.\n\nWork with software engineering teams to integrate model endpoints into user-facing products and internal applications.\n\nCollaborate with cybersecurity, architecture, legal, privacy, and governance teams.\n\nDefine MLOps engineering standards, design principles, coding practices, and operational requirements.\n\nConduct architecture reviews, code reviews, and production-readiness assessments.\n\nMachine Learning Experience :\nSignificant experience in MLOps, platform engineering, DevOps, site reliability engineering, cloud engineering, or production machine learning.\n\nProven experience deploying and operating machine learning models in production.\n\nStrong hands-on experience with AWS services and cloud architecture.\n\nStrong hands-on experience with Databricks, including MLflow, model registries, jobs, clusters, permissions, and model-serving capabilities.\n\nExperience building CI/CD pipelines using tools such as GitHub Actions, GitLab CI/CD, Jenkins, Azure DevOps, or AWS CodePipeline, Docker and containerized application deployment.\n\nExperience with Kubernetes and managed container platforms such as Amazon EKS or ECS.\n\nProficiency in Python and experience building production-quality APIs and inference services.\n\nExperience with REST APIs, asynchronous processing, event-driven architectures, and distributed systems.\n\nStrong knowledge of Infrastructure as Code, preferably Terraform.\n\nExperience with model versioning, artifact management, experiment tracking, and deployment promotion workflows.\n\nExperience implementing monitoring, logging, tracing, alerting, and production support processes.\n\nStrong understanding of high availability, disaster recovery, fault tolerance, and rollback strategies.\n\nKnowledge of cloud networking, IAM, secrets management, encryption, and secure software delivery.\n\nTraining and inference workflows\n\nOnline and batch inference\n\nModel serialization and packaging\n\nFeature engineering and feature consistency\n\nModel validation and evaluation,l drift and data drift\n\nModel explainability and reproducibility\n\nGPU-based model serving\n\nEmbeddings and vector databases\n\nRetrieval-augmented generation\n\nPrompt management and versioning\n\nLLM evaluation and guardrails\n\nToken limits, context management, and inference cost optimization\n\nResponsible AI, content safety, and human-in-the-loop controls\n\nThis role does not necessarily require the candidate to develop new machine learning algorithms. However, the candidate must be able to understand model behavior, deployment constraints, performance characteristics, and operational risks.\nPreferred Qualifications:\nBachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or a related discipline, or equivalent professional experience.\n\n12+ years of total experience\n\nExperience deploying generative AI or LLM-based applications in production.\n\nExperience with Amazon Bedrock, SageMaker, Databricks Mosaic AI, or similar AI platforms.\n\nExperience with vector-search technologies such as Databricks Vector Search, OpenSearch, Pinecone, Weaviate, Milvus, or pgvector.\n\nExperience with LLM application frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent orchestration tools.\n\nExperience with observability platforms such as Datadog, Grafana, Prometheus, CloudWatch, OpenTelemetry, or Splunk.\n\nExperience applying SRE practices to machine learning and AI systems.\n\nExperience operating platforms in regulated, enterprise, or data-sensitive environments.\n\nRelevant AWS, Databricks, Kubernetes, or cloud architecture certifications.\n\nWhat Success Looks Like\nWithin the first 6 to 12 months, the successful candidate will:\nEstablish a standardized and automated path from model development to production.\n\nReduce the time required to deploy a new model or LLM service.\n\nEnable repeatable deployments across development, staging, and production.\n\nProvide reliable and secure endpoints through which users and applications can interact with AI.\n\nImplement automated rollback and recovery for failed deployments.\n\nEstablish monitoring for service health, model performance, quality, security, and cost.\n\nImprove availability, deployment frequency, change-failure rate, and recovery time.\n\nCreate reusable platform components that increase engineering productivity.\n\nEstablish production-readiness standards for machine learning and generative AI services.\n\nExample Performance Indicators\nModel deployment lead time\n\nDeployment frequency\n\nPercentage of automated deployments\n\nChange-failure rate\n\nMean time to detect incidents\n\nMean time to recover\n\nEndpoint availability\n\nP95 and P99 inference latency\n\nModel rollback success rate\n\nRecovery-test success rate\n\nInfrastructure provisioning time\n\nCost per inference request\n\nPercentage of production models with complete lineage and monitoring\n\nNumber of security or compliance exceptions\n\nInternal developer and data scientist satisfaction\n\nIdeal Candidate Profile\nYou are a pragmatic platform engineer who understands that deploying a model is only the beginning. You think about security, reliability, monitoring, recovery, cost, governance, and the end-user experience from the start.\nYou are comfortable moving between cloud architecture, infrastructure automation, CI/CD pipelines, Python service...","description_format":"text","description_chars":13243,"description_truncated":true,"requirements":{"experience_years_min":12,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":true},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Laptops","Computer Peripherals","3D Printers & Additive Manufacturing","Printers & Scanners"],"lifecycle":[{"event":"open","at":"2026-08-24T00:00:00Z"}],"visa":[],"liveness":{"score":10,"band":"cold","label":"Long shot","p_open":1,"p_active":0.36,"p_room":0.28,"age_days":56,"expected_fill_days":22,"reasons":["conf:22","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-19T05:45:15Z"},"pay":null,"html_url":"https://alion.io/job/hp-inc-senior-machine-learning-engineer","json_url":"https://alion.io/job/hp-inc-senior-machine-learning-engineer.json","meta":{"generated_at":"2026-10-19T18:35:28Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":14454,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-20T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":72},"rest":"https://alion.io/mcp/rest/get_company?id=72"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fhp-inc-senior-machine-learning-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fhp-inc-senior-machine-learning-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fhp-inc-senior-machine-learning-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/hp-inc-senior-machine-learning-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fhp-inc-senior-machine-learning-engineer"}]}