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Sr Machine Learning Engineer
Amgen
Hyderabad
3-5 years
Today
$25.3K–39.8K/yr
Full-time
Onsite
Skills Required
LLM
RAG
LangChain
Vector Database
OpenAI SDK
Semantic Kernel
prompt-engineering
agent frameworks
Kubeflow
SageMaker Pipelines
MLOps
feature stores
model registries
experiment-tracking
REST
Description
Seeking a Sr Machine Learning Engineer to lead the design and scaling of end-to-end machine-learning and generative-AI platforms, enabling secure and cost-effective model deployment and monitoring.
Company: Amgen
Role: Sr Machine Learning Engineer
Experience
- 3-5 years in AI/ML and enterprise software
Responsibilities
- Engineer end-to-end ML pipelines including data ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration and automated promotion
- Harden research code into production-grade micro-services with Docker/Kubernetes and expose secure APIs
- Build and maintain full-stack AI applications integrating model services with UI components and workflow engines
- Optimize performance and cost at scale by selecting algorithms and tuning GPU/CPU auto-scaling policies
- Instrument observability with real-time metrics, distributed tracing, drift and bias detection, and user-behavior analytics
- Embed security and responsible-AI controls including data encryption, access policies, lineage tracking, explainability and bias monitoring
- Contribute reusable platform components such as feature stores, model registries, and experiment-tracking libraries
- Perform exploratory data analysis and feature ideation on complex datasets
- Partner with data scientists to prototype and benchmark algorithms and co-own model-performance KPIs
- Establish platform strategy and define technical standards
- Partner with DevOps, Security, Compliance and Product teams to deliver enterprise-grade AI developer experience
Additional Responsibilities
- Translate complex technical concepts into concise, outcome-oriented narratives
- Model total cost of ownership versus net present value and present trade-offs to executives
- Evangelize best practices to raise engineering velocity across squads
More Skills
machine-learning algorithms, regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures, CNNs, RNNs, transformers, RAG techniques, AI SaaS/PaaS, GenAI tooling, prompt-engineering DSLs, Python, Java, Docker, Kubernetes, AWS, Azure, GCP, GitHub Actions, Bedrock
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