About the Role

Machine Learning Engineers work on key AI initiatives including agentic systems for real-time agent support, quality improvement for LLM-powered agents, and insight extraction from enterprise data using advanced language models and retrieval methods.

Responsibilities

Lead the creation and implementation of advanced AI agent systems, shaping architecture and modeling strategies.
Design integrated workflows that combine real-time support, information retrieval, logical reasoning, summarization, and automated actions in production environments.
Build, deploy, and refine large language model-based systems, including retrieval-augmented generation, multi-agent coordination, and domain-specific adaptations.
Enhance agent capabilities in reasoning, planning, and tool interaction for practical AI applications.
Develop comprehensive evaluation methods for complex systems using offline testing, live experiments, and LLM-based assessment techniques.
Identify and resolve real-world issues such as false outputs, retrieval inaccuracies, incorrect tool usage, unstable prompts, and failures in multi-step logic.
Establish and monitor performance indicators—including accuracy, factual consistency, task success, response time, cost, and resilience—to improve system dependability.
Tune AI systems for efficiency, speed, security, and cost-effectiveness in live deployments.
Work closely with product, frontend, and backend teams to embed AI features smoothly into the platform.
Guide engineering team members, support technical direction, and help define the future path of AI system development.

Compensation

Base salary, equity, and comprehensive benefits included; actual pay varies by experience, skills, location, and local regulations.

Work Arrangement

Hybrid — available in the US and Canada

About the role

Machine Learning Engineers work across several high-impact AI initiatives. Final team placement is determined based on experience, strengths, and business needs. Current focus areas include: Agentic Assist: Lead and build next-generation agentic AI systems that augment contact center agents in real time. This track requires strong pre-LLM ML foundations, deep expertise in LLMs and modern prompting techniques, a rapid prototyping mindset, and a proven ability to translate cutting-edge research into scalable, production-grade systems. Agent & System Quality: Design evaluation frameworks and improve the reliability, robustness, and performance of LLM-powered agents. This includes diagnosing and mitigating failure modes such as hallucinations, retrieval errors, tool misuse, context drift, prompt brittleness, and multi-step reasoning breakdowns, while defining measurable quality metrics (e.g., accuracy, faithfulness, task completion, latency, and cost) for complex, non-deterministic systems. Insights: Architect and scale LLM and retrieval-augmented generation pipelines that ground models in enterprise data. This track focuses on building high-performance ML systems that process complex data, extract structured insights, and deliver real-time, actionable intelligence at scale.

Recruiting Scam Warning

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