Backend GenAI Engineer for GenAI Web APP
Budget: $15 – $25 USD
Job Description:
We’re seeking a skilled Backend Engineer to develop a web API for a GenAI web application. The ideal candidate should have a strong foundation in Python, experience with vector databases, and a solid understanding of LLMs. Knowledge of prompt engineering and LLM optimization for embeddings and inference is essential. A cloud-first mindset is required to ensure the application is both cost-effective and scalable.
Core Skills:
Proficient in Python and frameworks such as Flask, Django, LlamaIndex, and LangChain.
Experience with vector databases (in-memory and persistent).
Familiarity with LLMs, including their best use cases (e.g., embeddings vs. inference) and optimization for accuracy, speed, and cost.
Understanding of prompt engineering strategies for effective model utilization.
Cloud-native application development and deployment.
Familiarity with OAuth2, JWT, or similar frameworks.
Desirable Skills:
Experience with scalable architecture design.
Knowledge of cost optimization techniques in cloud environments.
Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
Familiarity with microservices and serverless architecture.
Ability to execute performance tuning and monitoring, particularly for handling large datasets and ensuring low latency in semantic search queries.
Expectations:
The engineer should have expertise in working with large datasets, embedding models, vector databases, semantic search, API development, and API security.
The project should be approached with a focus on scalability, performance, cost-efficiency, security, and continuous improvement.
We’re seeking a skilled Backend Engineer to develop a web API for a GenAI web application. The ideal candidate should have a strong foundation in Python, experience with vector databases, and a solid understanding of LLMs. Knowledge of prompt engineering and LLM optimization for embeddings and inference is essential. A cloud-first mindset is required to ensure the application is both cost-effective and scalable.
Core Skills:
Proficient in Python and frameworks such as Flask, Django, LlamaIndex, and LangChain.
Experience with vector databases (in-memory and persistent).
Familiarity with LLMs, including their best use cases (e.g., embeddings vs. inference) and optimization for accuracy, speed, and cost.
Understanding of prompt engineering strategies for effective model utilization.
Cloud-native application development and deployment.
Familiarity with OAuth2, JWT, or similar frameworks.
Desirable Skills:
Experience with scalable architecture design.
Knowledge of cost optimization techniques in cloud environments.
Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
Familiarity with microservices and serverless architecture.
Ability to execute performance tuning and monitoring, particularly for handling large datasets and ensuring low latency in semantic search queries.
Expectations:
The engineer should have expertise in working with large datasets, embedding models, vector databases, semantic search, API development, and API security.
The project should be approached with a focus on scalability, performance, cost-efficiency, security, and continuous improvement.