We are looking for a backend engineer who can design, build, and operate highly reliable Node.js services on AWS that enable generativeAI capabilities across our products and internal workflows.
You will create scalable APIs, data pipelines, and serverless architectures that integrate large language model (LLM) services such as Amazon Bedrock, OpenAI, and opensource models, enabling teams to safely and efficiently leverage generative AI.
What Youll Actually Be Doing:
Design and implement REST/GraphQL APIs in Node.js/TypeScript to serve generativeAI features such as chat, summarization, and content generation.
Build and maintain AWS native architectures using Lambda, API Gateway, ECS/Fargate, DynamoDB, S3, and Step Functions.
Integrate and orchestrate LLM services (Amazon Bedrock, OpenAI, self hosted models) and vector databases (Amazon Aurora pgvector, Pinecone, Chroma) to power Retrieval Augmented Generation (RAG) pipelines.
Create secure, observable, and cost efficient infrastructure as code (CDK/Terraform) and automate CI/CD with GitHub Actions or AWS CodePipeline.
Implement monitoring, tracing, and logging (CloudWatch, XRay, OpenTelemetry) to track latency, cost, and output quality of AI endpoints.
Collaborate with ML engineers, product managers, and frontend teams in agile sprints; participate in design reviews and knowledge sharing sessions.
Establish best practices for prompt engineering, model evaluation, and data governance to ensure responsible AI usage.
You will create scalable APIs, data pipelines, and serverless architectures that integrate large language model (LLM) services such as Amazon Bedrock, OpenAI, and opensource models, enabling teams to safely and efficiently leverage generative AI.
What Youll Actually Be Doing:
Design and implement REST/GraphQL APIs in Node.js/TypeScript to serve generativeAI features such as chat, summarization, and content generation.
Build and maintain AWS native architectures using Lambda, API Gateway, ECS/Fargate, DynamoDB, S3, and Step Functions.
Integrate and orchestrate LLM services (Amazon Bedrock, OpenAI, self hosted models) and vector databases (Amazon Aurora pgvector, Pinecone, Chroma) to power Retrieval Augmented Generation (RAG) pipelines.
Create secure, observable, and cost efficient infrastructure as code (CDK/Terraform) and automate CI/CD with GitHub Actions or AWS CodePipeline.
Implement monitoring, tracing, and logging (CloudWatch, XRay, OpenTelemetry) to track latency, cost, and output quality of AI endpoints.
Collaborate with ML engineers, product managers, and frontend teams in agile sprints; participate in design reviews and knowledge sharing sessions.
Establish best practices for prompt engineering, model evaluation, and data governance to ensure responsible AI usage.
Requirements:
Available working some US hours
Proficient in Hebrew and English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment – Must
4+ years professional experience building production services with Node.js/TypeScript.
3+ years hands on with AWS, including Lambda, API Gateway, DynamoDB, and at least one container service (ECS, EKS, or Fargate).
Experience integrating third party or cloud native LLM services (e.g., Amazon Bedrock, OpenAI API) into production systems.
Strong understanding of RESTful design, GraphQL fundamentals, and event driven architectures (SNS/SQS, EventBridge).
Proficiency with infrastructure as code (AWS CDK, Terraform, or CloudFormation) and CI/CD pipelines.
Familiarity with secure coding, authentication/authorization patterns (Cognito, OAuth), and data privacy best practices for AI workloads.
Technical Environment:
Languages: TypeScript, JavaScript, SQL
Frameworks & Libraries: Express.js, Fastify, Apollo Server, LangChainJS, AWS SDK v3
Datastores: DynamoDB, Aurora (Postgres + pgvector), Redis, S3
Infra & DevOps: AWS Lambda, API Gateway, ECS/Fargate, Step Functions, CDK, Terraform, Docker, GitHub Actions
AI Stack: Amazon Bedrock, OpenAI API, HuggingFace Inference Endpoints, Pinecone, Chroma.
Who You Are
You have experience building Retrieval Augmented Generation (RAG) systems or knowledge base chatbots.
You're Hand on with vector databases such as Pinecone, Chroma, or pgvector on Postgres/Aurora.
Have AWS certification (Developer, Solutions Architect, or Machine Learning Specialty).
Experience with observability tooling (Datadog, New Relic) and cost optimization strategies for AI workloads.
Background in microservices, domain driven design, or event sourcing patterns.
Available working some US hours
Proficient in Hebrew and English both written and verbal, sufficient for achieving consensus and success in a remote and largely asynchronous work environment – Must
4+ years professional experience building production services with Node.js/TypeScript.
3+ years hands on with AWS, including Lambda, API Gateway, DynamoDB, and at least one container service (ECS, EKS, or Fargate).
Experience integrating third party or cloud native LLM services (e.g., Amazon Bedrock, OpenAI API) into production systems.
Strong understanding of RESTful design, GraphQL fundamentals, and event driven architectures (SNS/SQS, EventBridge).
Proficiency with infrastructure as code (AWS CDK, Terraform, or CloudFormation) and CI/CD pipelines.
Familiarity with secure coding, authentication/authorization patterns (Cognito, OAuth), and data privacy best practices for AI workloads.
Technical Environment:
Languages: TypeScript, JavaScript, SQL
Frameworks & Libraries: Express.js, Fastify, Apollo Server, LangChainJS, AWS SDK v3
Datastores: DynamoDB, Aurora (Postgres + pgvector), Redis, S3
Infra & DevOps: AWS Lambda, API Gateway, ECS/Fargate, Step Functions, CDK, Terraform, Docker, GitHub Actions
AI Stack: Amazon Bedrock, OpenAI API, HuggingFace Inference Endpoints, Pinecone, Chroma.
Who You Are
You have experience building Retrieval Augmented Generation (RAG) systems or knowledge base chatbots.
You're Hand on with vector databases such as Pinecone, Chroma, or pgvector on Postgres/Aurora.
Have AWS certification (Developer, Solutions Architect, or Machine Learning Specialty).
Experience with observability tooling (Datadog, New Relic) and cost optimization strategies for AI workloads.
Background in microservices, domain driven design, or event sourcing patterns.
This position is open to all candidates.
