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Applied AI Engineer

Acunor InfotechNJ🇺🇸United StatesPosted 26 Aug 2026

Why This Role Stands Out

This remote Applied AI Engineer role offers a fantastic opportunity to build and deploy cutting-edge LLM-powered agents and workflows on a robust AWS-native platform. You'll thrive here if you're a mid-senior engineer eager to turn innovative AI concepts into reliable production systems, gaining valuable experience with Amazon Bedrock and its advanced capabilities. Apply now to shape the future of AI applications from anywhere in the US or Canada!

Quick Overview

Seniority
Mid Senior
Work mode
Remote
Location
NJ, United States
Posted
16 hours ago
API GatewayAWSCDKHIPAALLMPythonTerraformTypeScript

Job Description

Applied AI Engineer
Location: Remote United States, Canada
Work Hours: Must work Pacific Time (PST/PDT) business hours
Employment Type: Long-Term Contract
About the Role
We are seeking a hands-on Applied AI Engineer to design, build, evaluate, and productionize LLM-powered agents and AI workflows.
This is a production engineering role rather than a research-only or proof-of-concept position. You will turn business use cases into reliable AI systems using an AWS-native platform with Amazon Bedrock as the foundation-model layer.
Engineers will work within standardized platform templates, CI/CD pipelines, guardrails, and evaluation frameworks while building AI applications that are reliable, observable, measurable, and ready for production.
Key Responsibilities
  • Design, build, and deploy LLM-powered agents, workflows, and applications using Amazon Bedrock.
  • Work with Bedrock capabilities including Bedrock Agents, Knowledge Bases, Guardrails, and multiple foundation models.
  • Build agentic workflows incorporating RAG, tool/function calling, APIs, and enterprise data sources.
  • Develop using standardized AI platform templates, CI/CD pipelines, guardrails, and evaluation harnesses.
  • Build and maintain LLM evaluation suites as an integral part of the engineering lifecycle.
  • Work with golden datasets, regression testing, and LLM-as-a-judge evaluation approaches.
  • Implement prompt and context engineering strategies for reliable production behavior.
  • Develop structured outputs, retries, fallbacks, and graceful degradation mechanisms.
  • Integrate AI agents with enterprise applications and data through APIs and AWS services such as Lambda, Step Functions, SQS/SNS, and API Gateway.
  • Instrument AI applications for quality, latency, token consumption, cost, and operational telemetry.
  • Use services such as CloudWatch and Bedrock invocation metrics to monitor production systems.
  • Collaborate directly with business users to rapidly iterate, demonstrate solutions, gather feedback, and deliver production functionality.
Required Qualifications
  • 8+ years of software engineering experience.
  • At least 2.5+ years of experience building production LLM applications, including agents, RAG pipelines, and tool/function calling.
  • Strong programming skills in Python and/or TypeScript.
  • Strong API engineering experience, including API design, versioning, authentication, and error handling.
  • Strong practical experience with prompt engineering and context engineering.
  • Hands-on experience developing LLM evaluation approaches including golden datasets, LLM-as-a-judge, and regression suites.
  • Hands-on AWS experience.
  • Production experience with Amazon Bedrock strongly preferred.
  • Candidates with strong production experience using OpenAI/Anthropic APIs combined with AWS may also be considered if they can ramp quickly on Bedrock.
  • Experience building reliable, production-grade AI systems rather than only prototypes or research projects.
  • Ability to work full Pacific Time (PST/PDT) business hours.
  • Strong information-retrieval/RAG experience, including chunking strategies, embeddings, hybrid search, and re-ranking.
  • Experience with OpenSearch, pgvector, or Amazon Bedrock Knowledge Bases.
  • Healthcare data experience involving PHI, PII, or HIPAA-aware engineering.
  • LLM cost optimization experience, including model routing, prompt caching, batch inference, and throughput optimization.
  • Infrastructure-as-Code experience using Terraform or AWS CDK.
  • Container experience with ECS/EKS.

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