Home Based Prompt Engineering Jobs in New York (NOW HIRING)

• Join our AI & Engineering team in transforming technology platforms, driving innovation, and making a significant impact on our members' success.
• You will work alongside talented professionals reimagining and re-engineering operations and processes that are critical to our business — from underwriting and claims to member experience and risk management.
• Build & Deploy AI Solutions Partner with the Lead AI Solutions Architect and AI Data Engineer to design, build, and deploy secure, scalable AI solutions: APIs, services, pipelines, agents, containers, and serverless functions that meet availability, performance, and security requirements.
• Deploy AI workloads primarily using cloud-native patterns, including AWS ECS-based containerized applications.
• Build and operationalize LLM-enabled products including copilots, knowledge assistants, summarization engines, policy Q&A tools, and agentic workflows using Claude Code, OpenAI Codex, GitHub Copilot, AWS AgentCore Gateway, AWS AgentCore Harness, Databricks, and comparable LLM platforms.
• Implement RAG, knowledge base, and document intelligence patterns end-to-end: ingestion, chunking, embeddings, vector and hybrid search, retrieval evaluation, and telemetry.
• Deliver governed data and features for ML and GenAI — curated datasets, feature pipelines, and feature serving — supporting both training workflows and real-time inference with consistency, caching, backfill support, and latency SLOs.
• Apply thoughtful prompt and context patterns, tool/function calling, reusable agent skills, and agentic orchestration patterns.
• Ensure AI outputs are auditable, explainable, and compliant with applicable regulatory requirements (SOC 2, NAIC, GDPR).

• 5+ years of professional software engineering experience, with at least 1 year building and operating AI/ML systems in production.
• Proven hands-on experience with LLMs: prompt engineering, RAG pipelines, fine-tuning or adapting open-source models, function/tool calling, agent orchestration, and working with Claude, OpenAI/Codex, Gemini, or comparable models via API..
• Experience building and shipping agentic AI systems, multi-step agents, tool-use orchestration, reusable agent skills, autonomous workflow automation, and governed enterprise integrations in a production environment.
• Experience with AWS AgentCore Gateway, AWS AgentCore Harness, LangChain, LangGraph, or comparable agent frameworks is highly valuable..
• Strong Python engineering skills; ability to write clean, maintainable, production-grade code with FastAPI or similar frameworks, and package AI capabilities as APIs, services, workers, or containerized applications..
• Experience with AI/ML infrastructure: Databricks-based AI/ML workflows, knowledge bases, vector or hybrid search, feature pipelines, model serving patterns, container orchestration, Docker, and cloud-native deployment Familiarity with cloud-native AI workloads on AWS — including cost governance and performance tuning at scale.
• Experience implementing trust, safety, and governance controls in AI systems: PII handling, content filtering, access controls, and auditability.
• Comfort working in a delivery-oriented team: you ship, you measure, you iterate.
• Hands-on experience with AI-assisted software engineering tools such as Claude Code, OpenAI Codex, GitHub Copilot, or comparable developer productivity platforms.
• Experience creating reusable AI agent artifacts such as skill files, tool definitions, prompt templates, system instructions, evaluation datasets, and guardrail patterns.
• Experience with building reusable Github Workflow and make AI solutions part of the CI/CD.
• Knowledge and experience with the Software Development Life Cycle (SDLC), including both low-code/no-code platforms and traditional application development using Java/Python.
• Experience in building Serverless applications in AWS using AWS SAM Knowledge and experience with Terraform.

Demonstrates expertise in building and deploying AI solutions, particularly with LLMs and cloud-native patterns on AWS. Proficient in software engineering practices, including clean code development, CI/CD integration, and implementing governance controls in AI systems.

• AI/ML Systems Development
• AWS Cloud Services
• Python Programming
• LLM Prompt Engineering
• Container Orchestration

• AI Solutions Development
• Software Engineering
• Prompt Engineering
• Feature Pipelines
• Model Serving
• Containerized Applications
• Serverless Applications
• Data Governance
• CI/CD Integration
• SDLC Knowledge

• Team Collaboration
• Iterative Development
• Problem Solving

• AI Solutions
• Machine Learning
• Governance Controls
• Data Privacy
• Regulatory Compliance

• AWS AgentCore Gateway
• Databricks
• FastAPI
• Docker
• GitHub Copilot
• Claude Code
• OpenAI Codex
• Terraform
• LangChain
• AWS SAM