Sr Machine Learning Engineer

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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

Prepare for this role

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