Data & AI Engineer

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 Google Cloud Platform, 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 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.