Senior Generative AI Engineer
TypeFull-time job
LocationCanada
Posted2 hours ago
Senior Generative AI Engineer
Background
We are looking for a Senior Generative AI Engineer to design, build, and ship production-grade Generative AI and Agentic AI applications that delivery business value across the organization. This role is focused on building AI applications and services at scale. You will be responsible for building robust, secure, and highly scalable systems that integrate with leading cloud-based AI services.
You will work alongside product managers, designers, and other engineers as an individual contributor. You are expected to own features end-to-end, and to deliver high-quality, reusable code that scales beyond a single use case.
Key Responsibilities
Software Engineering and Execution
Design, build, and ship production-grade Generative and Agentic AI features and applications
Own features end-to-end from technical design through implementation, testing, deployment and operation
Build reusable, well-abstracted components and shared utilities (e.g., RAG building blocks, agent scaffolding, evaluation harnesses, prompt utilities) to enable faster delivery of future Generative and Agentic AI products
Build multi-agent systems using frameworks such as LangChain, LangGraph, Claude Agent SDK and Google ADK
Integrate with leading LLM and foundation model APIs, including Azure OpenAI, Google Vertex AI, and AWS Bedrock
Build Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, vector search, and re-ranking
Build clean, well-tested RESTful and/or gRPC APIs with a focus on reliability, security, and performance
Implement observability, tracing, evaluation, guardrails for Generative and Agentic AI applications
Deploy and operate services on major cloud providers (e.g., GCP, AWS, and Azure) leveraging managed services
Participate actively in code reviews and design discussions, sharing knowledge with peers
Required Qualifications
5-7 years of professional software engineering experience with at least 3 years of experience building AI/ML software products
Bachelor's degree in Computer Science or a related field (Master's degree preferred)
Strong proficiency in Python, with deep software engineering fundamentals (abstraction, modularity, system design, testing, performance)
Hands-on experience building and shipping Generative and Agentic AI applications, including LLM integration, prompt engineering, and/or agentic workflows
Practical experience integrating cloud-hosted LLM APIs such as Azure OpenAI, Vertex AI, and/or AWS Bedrock
Experience with agent frameworks (e.g., LangChain, LangGraph, Google ADK, Claude Agent SDK) and vector databases (e.g., Pinecone, Weaviate, pgvector, Open Search, AlloyDB)
Hands-on experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Azure
Solid understanding of API design, distributed systems, and cloud-native architecture
Track record of taking systems from design through production deployment and operation
Preferred Qualifications
Experience with containerization and orchestration (Docker, Kubernetes)
Knowledge of Generative AI Risk Management frameworks (NIST RFM)
Experience supporting developer platforms or internal tooling
Originally posted on Himalayas
Background
We are looking for a Senior Generative AI Engineer to design, build, and ship production-grade Generative AI and Agentic AI applications that delivery business value across the organization. This role is focused on building AI applications and services at scale. You will be responsible for building robust, secure, and highly scalable systems that integrate with leading cloud-based AI services.
You will work alongside product managers, designers, and other engineers as an individual contributor. You are expected to own features end-to-end, and to deliver high-quality, reusable code that scales beyond a single use case.
Key Responsibilities
Software Engineering and Execution
Design, build, and ship production-grade Generative and Agentic AI features and applications
Own features end-to-end from technical design through implementation, testing, deployment and operation
Build reusable, well-abstracted components and shared utilities (e.g., RAG building blocks, agent scaffolding, evaluation harnesses, prompt utilities) to enable faster delivery of future Generative and Agentic AI products
Build multi-agent systems using frameworks such as LangChain, LangGraph, Claude Agent SDK and Google ADK
Integrate with leading LLM and foundation model APIs, including Azure OpenAI, Google Vertex AI, and AWS Bedrock
Build Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, vector search, and re-ranking
Build clean, well-tested RESTful and/or gRPC APIs with a focus on reliability, security, and performance
Implement observability, tracing, evaluation, guardrails for Generative and Agentic AI applications
Deploy and operate services on major cloud providers (e.g., GCP, AWS, and Azure) leveraging managed services
Participate actively in code reviews and design discussions, sharing knowledge with peers
Required Qualifications
5-7 years of professional software engineering experience with at least 3 years of experience building AI/ML software products
Bachelor's degree in Computer Science or a related field (Master's degree preferred)
Strong proficiency in Python, with deep software engineering fundamentals (abstraction, modularity, system design, testing, performance)
Hands-on experience building and shipping Generative and Agentic AI applications, including LLM integration, prompt engineering, and/or agentic workflows
Practical experience integrating cloud-hosted LLM APIs such as Azure OpenAI, Vertex AI, and/or AWS Bedrock
Experience with agent frameworks (e.g., LangChain, LangGraph, Google ADK, Claude Agent SDK) and vector databases (e.g., Pinecone, Weaviate, pgvector, Open Search, AlloyDB)
Hands-on experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Azure
Solid understanding of API design, distributed systems, and cloud-native architecture
Track record of taking systems from design through production deployment and operation
Preferred Qualifications
Experience with containerization and orchestration (Docker, Kubernetes)
Knowledge of Generative AI Risk Management frameworks (NIST RFM)
Experience supporting developer platforms or internal tooling
Originally posted on Himalayas
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