AI Engineer

. Design and develop AI-enabled features for web-based workflow applications using LLMs, GenAI, and agentic AI patterns.

Posted 5/7/2026full-timeRemote • 🇺🇸 United StatesMid-LevelSenior💰 $100,000 - $125,000 per yearWebsite

Tools & technologies

AWSAzureCloudCyber SecurityDockerGoogle Cloud PlatformJavaJavaScriptNode.jsNumpyPandasPythonReactScikit-LearnSDLCTypeScript

Key responsibilities & impact

Design and develop AI-enabled features for web-based workflow applications using LLMs, GenAI, and agentic AI patterns.
Build RAG pipelines using application data, policy documents, user manuals, help cards and business process publications.
Implement agentic workflows using orchestration frameworks (LangGraph, Agno, Haystack, CrewAI) for AI based application assistants.
Implement prompt engineering strategies and integrate AI/LLM APIs (OpenAI, Bedrock).
Architect full-stack AI features with reliable API integrations, error handling, and graceful degradation.
Establish LLM observability (cost, latency, quality) and responsible AI guardrails (hallucination detection, output validation, CUI safeguards).
Maintain full-stack applications (including Web Application development using modern technologies such as React, TypeScript, NodeJS, Python and Java).
Address security vulnerabilities and ensure compliance with cybersecurity standards.
Participate in code reviews, CI/CD pipelines, and collaborative development workflows.
Document technical implementations and stay current with AI and ML developments.

What you’ll need

The ability to obtain, maintain and access classified information at the client level.
5+ years of experience with software implementation across all aspects of the SDLC.
1+ years hands-on experience developing generative AI / LLM-powered applications.
Expert-level proficiency in Python; strong JavaScript proficiency.
Experience with RESTful APIs and integrating AI/LLM APIs (OpenAI, Bedrock).
Experience with prompt engineering techniques (few-shot, chain-of-thought, system prompts).
Experience with LLM orchestration frameworks such as LangGraph, Agno, CrewAI, or similar.
Experience with RAG architectures, agentic patterns, and vector databases.
Strong understanding of ML fundamentals (supervised/unsupervised learning, model evaluation, feature engineering, NLP).
Experience with data analysis and ML libraries (pandas, NumPy, scikit-learn, or equivalent).
Experience with containerization (Docker) and at least one major cloud platform (AWS, Azure, or GCP).
Proficiency with Git version control and CI/CD pipelines.
Strong written and verbal communication skills.
Preferred: Experience with vector databases (OpenSearch, pgvector) and embedding models.
Experience with AWS GovCloud, AWS Bedrock, or Azure OpenAI Service.

Comp & perks

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Hard Skills & Tools

PythonJavaScriptRESTful APIsprompt engineeringLLM orchestration frameworksRAG architecturesdata analysisML librariescontainerizationcloud platforms

Soft Skills

strong written communicationstrong verbal communication