{"id":95172,"url":"https://alion.io/job/amgen-sr-machine-learning-engineer-2","title":"Senior Machine Learning Platform Engineer","company":{"id":4775,"name":"Amgen","domain":"amgen.com","url":"https://alion.io/company/amgen","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":79,"open_postings":436,"ghost_share":0.002,"stale_share":0.833,"repost_share":0.007,"time_to_fill_p50_days":27,"computed_at":"2026-10-16T05:45:00Z"}},"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":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":57000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon SageMaker","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"gRPC","optional":false},{"name":"Java","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","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":"Semantic Kernel","optional":false},{"name":"Feature Store","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-08-21T00:00:00Z","employer_posted_date":"2026-09-14","last_verified_at":"2026-10-16T19:08:41Z","board_verified":true,"closed_at":null,"days_open":56,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":56},"description":"Career Category\nEngineeringJob Description\nWe are seeking a Senior Machine Learning Platform Engineer to design, build and scale enterprise-grade machine-learning and generative-AI platform capabilities.\nThis role sits at the intersection of platform engineering, software engineering, MLOps and GenAI engineering. Rather than primarily developing individual machine-learning models or business-specific AI applications, you will build the shared platform capabilities, APIs, automation and engineering patterns that enable teams to develop, evaluate, deploy, govern and operate AI solutions at scale.\nYou will work closely with data scientists, ML engineers, application teams, DevOps, Security, Compliance and Product teams to create a secure, reliable and frictionless AI developer experience. The role combines hands-on engineering with technical leadership, helping define platform standards, reusable patterns and architecture for enterprise AI systems.\nRoles & Responsibilities\nDesign and build reusable ML and GenAI platform capabilities that support model development, experimentation, evaluation, deployment and production operations across multiple teams and use cases.\nBuild self-service platform services, APIs and automation that abstract infrastructure complexity and enable developers to provision and consume AI capabilities consistently.\nDevelop and maintain MLOps capabilities including experiment tracking, model and prompt registries, evaluation frameworks, deployment workflows and automated promotion across environments.\nBuild model-serving and inference capabilities that support classical ML models, deep-learning models and LLMs through scalable REST, gRPC or event-driven interfaces.\nDevelop platform capabilities for GenAI and agentic systems, including model access, prompt management, embeddings, vector search, Retrieval-Augmented Generation, tool integration and agent frameworks.\nEngineer integrations with major cloud-based AI and data platforms, using APIs, SDKs and managed services to provide reusable enterprise capabilities.\nBuild and maintain containerized platform services using Docker and Kubernetes, including deployment patterns, scaling strategies, service configuration and lifecycle management.\nDesign and implement platform APIs, SDKs, templates and shared libraries that establish standardized development patterns and reduce duplication across engineering teams.\nImplement comprehensive observability and operational monitoring, including logs, metrics, distributed tracing, service health, model/LLM usage, latency, errors and operational dashboards.\nImplement AI evaluation and quality-management capabilities, including automated evaluation pipelines, regression testing, model comparison and release-quality gates.\nBuild security and governance controls into platform capabilities, including authentication, authorization, secrets management, data access controls, auditability, lineage and responsible-AI controls.\nDesign platform mechanisms for usage metering, cost visibility and optimization, enabling teams to understand infrastructure and AI-service consumption.\nEngineer platform services for scalability, reliability and resilience, including retries, asynchronous processing, concurrency controls, fault tolerance and graceful failure handling.\nDevelop and improve CI/CD pipelines for AI platform services, reusable components and model-based workloads, including automated testing, artifact management and environment promotion.\nEvaluate new AI, ML and cloud technologies and determine when they should be introduced as shared platform capabilities versus application-specific solutions.\nPartner with data scientists and ML engineers to identify recurring development and operational challenges and convert them into reusable platform patterns and services.\nProvide technical guidance on architecture, scalability, performance, security and production-readiness for ML and GenAI workloads.\nParticipate in architecture reviews, code reviews, incident resolution and production troubleshooting across application, platform and infrastructure layers.\nCreate and maintain technical designs, architecture decision records, development standards, operational runbooks and platform documentation.\nHelp define the longer-term technical roadmap and engineering standards for ML and GenAI platform capabilities.\nMust-Have Skills\n3-5 years of experience in machine learning engineering, ML platform engineering, MLOps, backend engineering, cloud engineering or enterprise AI systems.\nStrong software-engineering fundamentals with experience building production-grade distributed services, APIs and reusable libraries.\nStrong programming skills in Python; experience with Java or another enterprise programming language is preferred.\nExperience designing and developing REST APIs, microservices and backend platform services.\nHands-on experience with Docker and Kubernetes or equivalent containerization and orchestration technologies.\nStrong understanding of MLOps and GenAIOps concepts, including experiment tracking, model lifecycle management, evaluation, deployment, monitoring and reproducibility.\nExperience with platforms and technologies such as MLflow, Kubeflow, SageMaker, Databricks or equivalent ML platforms.\nExperience building or integrating model-serving infrastructure for ML models and/or LLM-based applications.\nStrong understanding of modern GenAI architecture patterns, including LLM APIs, prompt management, embeddings, vector databases, RAG and agent-based systems.\nExperience with GenAI or agent frameworks such as LangChain, LangGraph, Semantic Kernel or equivalent frameworks.\nExperience integrating cloud-based AI/ML SaaS and PaaS services and building abstractions around those services for broader developer consumption.\nExperience with at least one major cloud platform such as AWS, Azure or GCP.\nFamiliarity with CI/CD, Git-based development, automated testing and release-management practices.\nUnderstanding of observability and distributed-system concepts, including logging, metrics, tracing, retries, timeouts and failure handling.\nUnderstanding of enterprise security patterns, including authentication, authorization, RBAC, OAuth/OIDC, secrets management and encryption.\nFamiliarity with data-governance and responsible-AI concepts, including lineage, explainability, access controls, model evaluation and bias monitoring.\nExperience working with relational and NoSQL databases and structured or unstructured data systems.\nAbility to design reusable platform abstractions rather than solving requirements through one-off application implementations.\nAbility to evaluate architectural options and communicate trade-offs across performance, scalability, reliability, security and cost.\nStrong collaboration and communication skills, with the ability to work across data science, engineering, infrastructure, security and product teams.\nPreferred Skills\nExperience building internal developer platforms, ML platforms or self-service engineering platforms.\nExperience with Infrastructure as Code such as Terraform.\nExperience designing multi-tenant platform services with resource isolation, quotas, governance and usage attribution.\nExperience with feature stores, model registries, evaluation platforms, AI gateways or centralized model-serving architectures.\nFamiliarity with event-driven architectures, message queues and asynchronous processing.\nExperience defining technical standards, architecture patterns and reusable engineering frameworks across multiple 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