Software AI Engineer – Agentic AI

Software AI Engineer – Agentic AI

Location:

New York, NY (ONSITE) FTE ONLY

• 10+ years of experience building large-scale distributed systems + strong experience with LLM systems, agentic workflows or advanced ML infrastructure

• 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.

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Languages:

Python, Go, Type Script

• APIs and services: REST, gRPC

• Cloud and infrastructure: AWS and/or GCP, Kubernetes

• Distributed systems: event-driven architectures, including Kafka

• Orchestration Frameworks:
Lang Graph, Lang Chain, Air Flow, etc

• Integration of commercial and open-source LLMs into agentic workflows

• Agent and orchestration frameworks such as Lang Chain, Llama Index, 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 Tensor Flow

• Strong schema, validation, and state management practices using tools such as Pydantic (Python) and Zod (Type Script)

• 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 autonomous 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.