{"id":580245,"url":"https://alion.io/job/amgen-sr-machine-learning-engineer-4","title":"Sr Machine Learning 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":437,"ghost_share":0.002,"stale_share":0.831,"repost_share":0.007,"time_to_fill_p50_days":27,"computed_at":"2026-10-17T05:46:00Z"}},"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":27000,"max_usd":60000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":29},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Google AI Studio","optional":false},{"name":"Kubernetes","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Red Teaming","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Transformers","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"FinOps","optional":true},{"name":"Knowledge Distillation","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"Model Distillation","optional":true},{"name":"PEFT","optional":true},{"name":"Quantization","optional":true},{"name":"Time Series Forecasting","optional":true}],"status":"live","first_seen_at":"2026-08-15T00:00:00Z","employer_posted_date":"2026-08-15","last_verified_at":"2026-10-18T16:55:47Z","board_verified":true,"closed_at":null,"days_open":64,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":64},"description":"Career Category\nInformation SystemsJob Description\nSenior Machine Learning Engineer, AI Studio\nCAREER LEVEL: GCF 5 - Specialist\nCAREER TRACK: Individual Contributor\nPRIMARY SCOPE: End-to-end ownership of a small AI asset orsubstantialtechnical workstream\nORGANIZATION: Applied AI | AI Studio\nABOUT AMGEN\nAmgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier,fullerand longer. We discover, develop,manufactureand deliver innovative medicines to help millions of patients. Amgen helpedestablishthe biotechnology industry more than 40 years ago andremainsat thecutting edgeof innovation, using technology and human genetic data to push beyond what is known today.\nABOUT THE ROLE\nRole Description:\nThe Senior Machine Learning Engineer positionoffers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) - organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company toidentifythe right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle-from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact.You willbepart of AI Studio anddefine and own AI assetsor substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition,adoptionand measurable outcome.\nYou will remain hands-on while leading decisions across software, statistics, classical ML, deep learning, NLP, GenAI, RAG, bounded agents, data and knowledge pipelines, APIs,MLOps/LLMOps, security,governanceand operations. Within Applied AI-AI Studio turn prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation,observabilityand lifecycle operations.\nRoles & Responsibilities:\nDefine the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operatingownerand measurable technical and business outcomes.\n\nMap rules, exceptions, datadependenciesand human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG,agentsor a manual approach.\n\nOwn production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, securityzonesand human review.\n\nLead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agenttoolsand workflow orchestration.\n\nEstablish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expertadjudicationand release thresholds.\n\nEstablishMLOps/LLMOpsfor lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity,costand runbooks.\n\nCoordinate security, privacy, Responsible AI, Quality, legal, model-risk andGxPcontrols; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices.\n\nBasic Qualifications and Experience:\nBachelor’s/Master’sdegree with 8 - 13 years of experience in Computer Science,ITor related field.\nFunctional Skills:\nAdvanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, designreviewand end-to-end architecture.\n\nAdvanced statistics,MLand experimental design: EDA, feature engineering, supervised and unsupervised learning, predictivemodelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified.\n\nDeep learning,NLPand foundation models: Selection and production use of neural, transformer, embedding, vision,documentand multimodal approaches, including fine-tuning versus prompting, latency,privacyand cost trade-offs.\n\nGenAI, RAG,knowledgeand agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarialevaluationand human control.\n\nData, knowledge,cloudand AI operations:Trustworthybatch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes,MLOps/LLMOps, SLOs and lifecycle operations.\n\nResponsible AI and regulated delivery: Risk assessment, least privilege, threatmodelling, red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicableGxPcontrols.\n\nMust-Have Skills:\nDemonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome.\n\nStrong hands-onproficiencyin Python and SQL, with experience designing production software,servicesand evaluation pipelines.\n\nAdvancecapability in at least one role-defining pillar-Applied ML, GenAI/RAG/agentsor ML platform/MLOps-plus credible depth across the production lifecycle.\n\nGood-to-Have Skills:\nAdvanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survivalmethodsor drift-aware retraining.\n\nAdvanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT orLoRA, fine-tuning, distillation, quantization, routing,cascadesor inference optimization.\n\nCloud,platformand AI operations: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code,MLflow, Airflow, Kubeflow, observability and FinOps.\n\nHuman-AI and regulated delivery: Experience with review, correction, approval, accessibility, uncertainty communication, workflow automation andGxP-relevant or validated systems.\n\nSoft Skills:\nStrong product thinking and ability to connect technical decisions to user, workflow, risk,costand business value.\n\nTechnical leadership, mentoring and constructive challenge whileremaininghands-on.\n\nExcellent analytical judgment and clear communication of evidence, uncertainty,trade-offsand limitations.\n\nCross-functional leadership across business, product, architecture,engineeringand control functions.\n\nOwnership, resilience and continuous improvement through incidents,feedbackand measured outcomes.\n\nEQUAL OPPORTUNITY STATEMENT\nAmgen is an Equal Opportunity employer and will consider you without regard to your race,colour, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.\n.","description_format":"text","description_chars":7141,"description_truncated":false,"requirements":{"experience_years_min":8,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Prescription Drugs","Biologics & Biosimilars","Immunology & Inflammation Therapeutics","Cardiometabolic Therapeutics"],"lifecycle":[{"event":"open","at":"2026-09-09T21:18:16Z"}],"visa":[],"liveness":{"score":11,"band":"cold","label":"Long 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