Senior AI Research Engineer - Agentic Systems & Platform

Accenture; Boston (MA); On-site; USD 187,000 - 311,000; Full time

Job summary

Accenture is seeking a Senior Advanced AI Research Engineer to bridge AI systems research and production platform engineering in a heavily hands-on role. You will prototype, benchmark, and translate research into scalable Python/Node.js components used by enterprise workloads.

The role emphasizes building production-grade services, contributing interfaces, and ensuring observability, with ties to broad client deployment needs and platform roadmaps.

Qualifications

• Bachelor's degree in CS, CS Engineering, or related field.
• 5 years of Python and/or Node.js/TypeScript building production backend services or AI/ML systems.
• 5 years hands-on experience with AI/ML systems (LLMs, agent frameworks, retrieval/memory architectures).
• 6+ years across research and production contexts, shipping research into running systems.

Responsibilities

• Investigate active innovation frontiers in agentic AI systems (memory, routing, autonomy).
• Design benchmarking and evaluation methodologies for production-relevant agentic tasks.
• Translate research into production-grade Python/Node.js services and components.
• Instrument prototypes with observability for reproducibility and low-friction platform integration.
• Collaborate with platform engineers to integrate capabilities into production systems.
• Communicate findings and mentor engineers on experimental methodology and production practices.

Skills

Python

Node.js/TypeScript

AI/ML systems

research to production

async Python

distributed tracing

Education

Bachelor's degree in CS/Engineering

Master's or PhD preferred

Tools

FastAPI

Kubernetes

OpenTelemetry

Job description

Accenture is seeking a Senior Advanced AI Research Engineer to bridge AI systems research and production platform engineering in a heavily hands-on role. You will prototype, benchmark, and translate research into scalable Python/Node.js components used by enterprise workloads.

The role emphasizes building production-grade services, contributing interfaces, and ensuring observability, with ties to broad client deployment needs and platform roadmaps.