Machine Learning Engineer
Amgen
India - HyderabadPosted about 1 month agoDiscoveredMatch locked
Full time
Career Category
Engineering
Job Description
Machine Learning Engineer
We are seeking a
Machine Learning Engineer
to build, deploy and operate scalable machine-learning and generative-AI solutions at Amgen. Working at the intersection of software engineering and data science, you will help transform models and AI prototypes into reliable, secure and production-ready applications and services.
You will collaborate closely with data scientists, senior ML engineers, DevOps, Security, Compliance and Product teams to develop ML pipelines, model services and AI applications using modern cloud, MLOps and GenAI technologies. The role requires strong hands-on engineering skills, a solid understanding of machine-learning techniques and an interest in building enterprise-grade AI solutions.
Roles & Responsibilities
•
Develop end-to-end ML pipelines
covering data ingestion, feature engineering, evaluation, model registration and deployment using Kubeflow, SageMaker Pipelines or equivalent MLOps platforms.
•
Productionize machine-learning and GenAI models
by converting research and prototype code into reliable services, packaging applications using Docker and Kubernetes, and exposing models through secure REST, gRPC or event-driven APIs.
•
Build AI and GenAI applications
by integrating ML/LLM services with user interfaces, APIs, workflow engines and business-logic layers to deliver model-generated insights to end users.
•
Develop and implement GenAI solutions
using LLMs, prompt engineering, embeddings, vector databases, Retrieval-Augmented Generation (RAG) and agent-based frameworks.
•
Implement monitoring and observability
for production ML applications, including model-performance metrics, application logs, distributed tracing, data/model drift detection and operational dashboards.
•
Apply security and responsible-AI practices
including access controls, encryption, data lineage, model explainability, bias monitoring and secure handling of enterprise data.
•
Develop reusable ML platform components
such as model-serving templates, feature-engineering utilities, experiment-tracking integrations and common libraries that improve development efficiency across teams.
•
Perform exploratory data analysis and feature engineering
on structured and unstructured datasets to identify patterns, assess data quality and support model development.
•
Evaluate and benchmark machine-learning algorithms
including regression, tree-based models, clustering, time-series approaches, deep-learning architectures and LLM-based techniques based on business and technical requirements.
•
Collaborate with data scientists
to transition experiments and prototypes into scalable production implementations while maintaining model accuracy, reliability and reproducibility.
•
Participate in code reviews, testing and CI/CD practices
to ensure ML services meet engineering quality, maintainability and deployment standards.
•
Troubleshoot production ML systems
and work with platform and DevOps teams to resolve model, application, infrastructure and integration issues.
•
Document technical designs, implementation patterns and operational procedures
to support maintainability and knowledge sharing across engineering teams.
Must-Have Skills
•
2–5 years of experience
in machine learning, AI, data engineering or enterprise software development.
- Strong understanding of core
machine-learning algorithms
, including regression, classification, tree-based ensembles, clustering, dimensionality reduction and time-series modeling.
- Experience with GenAI/agent development frameworks such as
LangChain or equivalent frameworks
.
- Strong programming skills in
Python
, with experience developing production-quality software. Experience with
Java or another enterprise programming language
is preferred.
- Experience building and consuming
REST APIs, microservices and model-serving applications
.
- Hands-on experience with
Docker and Kubernetes
or equivalent containerization and orchestration technologies.
- Experience with at least one major cloud platform such as
AWS
, including managed AI/ML services.
- Familiarity with
MLOps and DevOps practices
, including CI/CD, experiment tracking, model registries, automated testing, model deployment and monitoring.
- Experience with tools or platforms such as
SageMaker, Bedrock, Kubeflow, MLflow, GitHub Actions or equivalent technologies
.
- Understanding of
model monitoring, data drift, model drift, explainability and responsible-AI concepts
.
- Familiarity with relational and NoSQL databases and experience working with structured and unstructured data.
- Strong software-engineering fundamentals, including
Git, testing, debugging, API design and object-oriented programming
.
- Ability to analyze technical requirements, evaluate implementation options and communicate engineering trade-offs effectively.
- Strong collaboration and communication skills, with the ability to work effectively with
data scientists, engineers, product teams and business stakeholders
.
- Ability to translate business requirements into practical machine-learning and AI solutions while balancing
model performance, scalability, reliability and cost
.
.
Not included in the source posting: benefits.
Skills
machine-learningartificial-intelligencegenerative-aidevopsmlopsci-cddockerkubernetesllmrest-apiawsgit
Who can apply
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