AI Prompt Engineer

Multiple AI roles_FDE–Agentic AI/AI Prompt Engineer/AI Lead Engineering/AI Architect / FDE – Healthcare_New York, Bay Area, Dallas, or Chicago

Locations: New York, Bay Area, Dallas, or Chicago

Work model: hybrid, with no fully remote option.

Travel: approximately 25% when necessary.

Office attendance: the exact weekly onsite schedule remains pending.

Compensation guidance: USD 150,000–200,000 annual base, with potential flexibility.

Full JDs:

JD1:Forward Deployed Engineer – Agentic AI

This role combines senior client engagement with hands-on engineering, translating business challenges into working AI solutions and taking them from rapid proofs of value toward MVP and production delivery.

Required background:

- 8+ years in full-stack engineering, solution architecture, or technical consulting, with significant client-facing delivery.

- Strong Python plus TypeScript, Java, or Go.

- Practical experience with LLMs, agent orchestration, tool calling, planning/execution loops, and memory patterns.

- RAG, embeddings, chunking, vector databases, and retrieval evaluation.

- APIs, data pipelines, enterprise integrations, and sound software architecture.

- Cloud-native engineering on AWS, Azure, or GCP, including containers, CI/CD, and observability.

- Ability to explain technical decisions and measurable business value to senior stakeholders.

Prioritize engineers who personally build solutions, develop evaluation methods, and address security, access controls, human oversight, reliability, latency, and cost. Production GenAI experience and integrations with platforms such as ServiceNow, Salesforce, SAP, Microsoft, Databricks, or Snowflake are preferred.

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JD2: AI Prompt Engineer / Agentic AI Engineer

This is a hands-on engineering role focused on developing agents that reason, use tools, and execute enterprise workflows.

Key requirements:

- Experience building LLM applications and agent workflows using Semantic Kernel, LangChain, LangGraph, AutoGen, CrewAI, or comparable frameworks.

- Hands-on MCP servers, tools, API orchestration, and enterprise connectivity.

- Python, TypeScript, or similar programming languages.

- Prompt optimization, agent planning, memory, context management, and multi-step execution.

- RAG, embeddings, vector databases, and knowledge grounding.

- Familiarity with Azure AI Foundry, Azure OpenAI, AWS Bedrock, Google Vertex AI, or comparable platforms.

- Agent evaluation, testing, deployment, monitoring, troubleshooting, security, and governance.

Enterprise copilots, AI assistants, and healthcare or regulated-system integrations are preferred. The supplied JD does not specify a minimum number of years, so please assess demonstrated engineering depth rather than imposing the FDE or platform-engineering experience threshold.

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JD3: AI Lead Engineering – Enterprise AI, Cloud & Platform Transformation

This role combines AI engineering with enterprise platform design, cloud transformation, and production operations.

Required experience:

- 12+ years across AI, DevOps, cloud engineering, SRE, or platform engineering.

- 3+ years of hands-on AI-stack experience.

- 5+ years designing enterprise platforms.

- 3+ years implementing AIOps or observability platforms.

- Enterprise cloud transformation, production AI/ML environments, and mission-critical or regulated platforms.

- Strong coordination with architecture, operations, security, and delivery teams.

Python is mandatory. Relevant technologies include GenAI, LLMs, RAG, AgentCore, MCP, AWS Bedrock, SageMaker, vector databases, model lifecycle management, and AWS/Azure/GCP. Experience with engineering copilots such as Kiro or GitHub Copilot is relevant.

Infrastructure automation, CI/CD, Kubernetes, Docker, service meshes, and tools such as Terraform, Datadog, Dynatrace, Splunk, Prometheus, and OpenTelemetry are useful. Specific tools are listed as preferred in several areas; do not require every named product. The required AIOps/observability experience should still be demonstrated.

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JD4: AI Architect / FDE – Healthcare

This role focuses on AI-enabled enterprise architecture, GCP engineering, and integration in a healthcare environment.

Required background:

- 12+ years of overall IT experience, with substantial cloud, enterprise, integration, or AI/ML architecture experience.

- Strong hands-on GCP and cloud-native architecture.

- Vertex AI, GenAI solution design, prompt engineering, model integration, governance, and production deployment considerations.

- Strong Java and Spring Boot, including microservices, REST APIs, security, and scala