3 days ago
Responsibilities
- Build and extend a production TypeScript agent harness covering runtime loops, tool dispatch, streaming, retries, state persistence, recovery, and reliability guardrails.
- Design context, memory, caching, and token-budgeting strategies for long-horizon agent work.
- Architect multi-agent and sub-agent systems with task decomposition, delegation, shared state, parallel execution, and result aggregation.
- Develop typed and testable agent tools for filesystem and shell access, code execution, web search, documents, and enterprise data connectors.
- Route across models and providers using a LiteLLM-based gateway while optimizing quality, latency, and cost.
- Engineer evaluated RAG pipelines using chunking, embeddings, vector search, hybrid retrieval, and reranking.
- Create structured model outputs, prompts, system instructions, and agent skills.
- Build offline and trajectory-level evaluation harnesses to measure agent accuracy, faithfulness, cost, latency, and tool use.
- Instrument production agents with tracing and observability, including token usage, cache hit rates, and cost tracking.
- Deploy maintainable software at scale in Azure, contribute to the software development lifecycle, pair with engineers, and review pull requests.
Requirements
- Strong TypeScript development experience, preferably in a Node.js backend context, including typed tool schemas, agent state, and structured outputs.
- Hands-on experience building agents with a modern framework or SDK; Vercel AI SDK experience is strongly preferred.
- Production experience with agent harnesses or runtime loops, including tool dispatch, error handling, repair, interrupt/resume, and long-running execution.
- Practical knowledge of context engineering, memory design, prompt and response caching, and multi-step cost and latency trade-offs.
- Experience designing multi-agent architectures, sub-agent orchestration, delegation boundaries, shared state, and failure handling.
- Deep practical knowledge of prompt engineering, agentic patterns, tool and function calling, and structured outputs.
- Experience routing multiple models and providers through a gateway such as LiteLLM.
- Experience building, optimizing, and evaluating RAG and retrieval pipelines.
- Strong PostgreSQL experience, including pgvector, hybrid or semantic search, and durable storage for agent state, memory, and traces.
- Experience designing behavior evaluations and using evidence to assess agent quality.
- Confident Git usage and experience working in a large shared monorepo such as Nx or Yarn workspaces.
- Desirable qualifications include SLM selection or fine-tuning, Langfuse or OpenTelemetry, sandboxed execution, enterprise connectors, full-stack TypeScript, Playwright, document generation, Python and FastAPI, ML or LLM research, classical ML and deep learning, Docker, durable queues, Redis, Azure AI services, CI/CD, and Terraform.
- Strong critical thinking, analytical rigor, attention to detail, communication, teamwork, and problem-solving skills are required.
Benefits
- Flexible work arrangements allowing employees to choose when, where, and how they work with their team and clients.
- Career progression, continuous learning, and leadership development opportunities.
- Inclusive workplace initiatives supporting Inclusion and Diversity and First Nations Prosperity.
- Four weeks of leave plus floating public holidays, birthday leave, and the option to purchase additional leave.
- Up to 26 weeks of parental leave for caregivers.
- Up to 20% travel and work visa sponsorship is available.
Tech Stack
AzureDockerFastAPIGitGitHub ActionsGraphQLNestJSNext.jsNode.jsPostgreSQLPythonReactRedisTerraformTypeScript
Categories
About PwC
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