Location: New York City, NY
Salary: $130,000.00 USD Annually - $135,000.00 USD Annually
Description: Job Details:
Job Role: AI Data Engineer
Location: New York, NY 10010
Duration: Full-Time job
Must Have Technical/Functional Skills
10+ years of experience building large-scale distributed systems + strong experience with LLM systems, agentic workflows or advanced ML infrastructure
AI engineers with recent NodeJS/Javascript/Typescript experience
Proven ownership of complex, cross-cutting agentic systems spanning multiple teams or products.
Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure.
Deep experience across the agentic AI stack, including planning, tool use, memory, and evaluation.
Fluency with AI-assisted and agentic development workflows.
Comfort operating in ambiguous problem spaces and translating them into shipped, reliable autonomous systems.
Ability to influence technical direction and align teams without formal authority.
Experience in workflow engines, async processing, queues, and streaming systems.
Languages: NodeJS/Javascript/Typescript,Python, Go,
APIs and services: REST, gRPC
Cloud and infrastructure: AWS and/or GCP, Kubernetes
Distributed systems: event-driven architectures, including Kafka
Orchestration Frameworks: LangGraph, LangChain, AirFlow, etc
Integration of commercial and open-source LLMs into agentic workflows
Agent and orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI, with strong judgment about when to use frameworks versus building lighter-weight primitives
Model-level work using PyTorch and the Hugging Face ecosystem (embeddings, fine-tuning, inference tooling), with some exposure to TensorFlow
Strong schema, validation, and state management practices using tools such as Pydantic (Python) and Zod (TypeScript)
Roles & Responsibilities
Drive technical direction for agentic AI initiatives, influencing architecture patterns, autonomy boundaries, and system design.
Design, build, and operate production-grade agentic AI systems used across multiple products.
Own and evolve shared agentic AI capabilities, including:
Agent frameworks and orchestration layers
Planning, tool use, and memory strategies
Retrieval and grounding (RAG) pipelines
LLM infrastructure, inference, and model gateways
Evaluation, observability, and safety tooling for au tonomous systems
Lead technical design reviews and help teams navigate tradeoffs involving autonomy, safety, reliability, scalability, and cost.
Partner across teams to deliver complex, cross-cutting agentic AI initiatives from concept to production.
Evaluate emerging models, techniques, and agentic patterns and translate them into practical, enterprise-ready improvements.
Mentor senior engineers and raise the technical bar for agentic AI development through example and influence.
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