Develop scalable software applications and services using modern cloud-native architectures.
Integrate large language models, APIs, databases, vector stores, and orchestration frameworks into production-ready applications.
Build and maintain AI-enabled workflows using agent frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, or similar technologies.
Implement retrieval-augmented generation patterns, including document ingestion, chunking, embedding generation, vector search, reranking, and response generation.
Partner with data science and ML teams to operationalize machine learning models and AI capabilities into software products.
Apply MLOps practices for model deployment, monitoring, versioning, evaluation, governance, and continuous improvement.
Develop reusable components, APIs, services, and integration patterns to accelerate AI solution delivery.
Define and implement robust cloud architectures, preferably on AWS, using serverless, containerized, or microservices-based approaches.
Implement observability, logging, monitoring, error handling, and performance optimization for AI and ML-enabled applications.
Evaluate and improve agent performance, including prompt quality, tool selection, response accuracy, latency, cost, and reliability.
Functional Skills
Must-Have Skills
Strong hands-on software engineering experience with Python, JavaScript/TypeScript, SQL/NoSQL, APIs, and cloud-native application development.
Experience building or integrating applications with generative AI, LLMs, agentic workflows, RAG patterns, or AI orchestration frameworks.
Working knowledge of ML lifecycle concepts, including model deployment, evaluation, monitoring, versioning, and automation.
Experience with CI/CD, testing, containerization, observability, and production-grade software delivery.
Strong problem-solving, communication, collaboration, and technical ownership skills.
Good-to-Have Skills
Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, or similar agent frameworks.
Experience with vector databases, embeddings, semantic search, reranking, prompt engineering, fine-tuning, or LLM evaluation.
Experience with MLOps tools such as MLflow, SageMaker, Databricks, Kubeflow, Airflow, or similar platforms.
Familiarity with serverless, microservices, event-driven architecture, Kubernetes, or managed cloud AI/ML services.
Knowledge of responsible AI, model governance, human-in-the-loop workflows, security, privacy, and regulated environments.
Education and Professional Certifications
Bachelor’s or Master's degree in computer science and engineering preferred, other Engineering field is considered; 7 to 12 years of software development experience
Cloud Computing certificate preferred
Soft Skills
Excellent analytical and troubleshooting skills.
Strong verbal and written communication skills
Ability to work effectively with global, virtual teams
High degree of initiative and self-motivation.
Ability to manage multiple priorities successfully.
Team-oriented, with a focus on achieving team goals
Strong presentation and public speaking skills.
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.
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