AI Engineer III

Frontline Education; United States; Remote; USD 100,000 - 110,000; Full time

Benefits offered by this job

Annual bonus

401(k) with company match

Comprehensive medical, dental, and视ion

Tuition reimbursement

Flexible time off

Wellness initiatives

Job summary

Frontline Education is seeking an AI Engineer III for a remote role in the United States. You’ll design, build, and ship production-grade AI features powered by modern LLMs, partnering with product, data, and CX teams to deliver measurable outcomes like faster resolution and higher CSAT.

Responsibilities include end-to-end development of LLM features, building RAG pipelines, enforcing safety/privacy, and integrating with cloud services.

Qualifications

• 4–6 years in applied ML/AI or backend engineering with measurable production impact.
• Strong Python and software engineering fundamentals (testing, types, CI/CD).
• Practical LLM experience: OpenAI/Anthropic, or cloud providers (AWS Bedrock, Azure OpenAI, GCP Vertex).
• Experience with at least one DL/LLM framework (PyTorch, Transformers) and one orchestration library (LangChain, LlamaIndex).
• RAG and data pipelines: embedding strategies, vector DBs, document QA.
• Monitoring/telemetry for AI systems (MLflow, Weights & Biases, Prometheus).
• Security & privacy awareness (PII handling, redaction).

Responsibilities

• Own end-to-end development of LLM features: problem framing, data prep, prototyping, evaluation, deployment, and monitoring.
• Build retrieval-augmented generation (RAG) pipelines with vector search and document orchestration.
• Implement prompt strategies, tool use/function calling, and guardrails for safety, bias, and privacy.
• Integrate models in production services with authentication, rate limiting, and observability.
• Stand up evals and experiment frameworks with clear success metrics.
• Collaborate with DevOps/MLOps to automate CI/CD and data/version management.
• Embed with CX/Support to mine tickets and transcripts for training data.
• Instrument user journeys and run A/B tests and ship iterative improvements.
• Build feedback loops and human-in-the-loop fallbacks to protect quality.

Skills

Python

LLM experience

Production impact

MLOps / DevOps

Security & privacy

Tools

PyTorch

LangChain

FAISS

OpenSearch

Docker

Kubernetes

Job description

AI Engineer III

This role will be posted through 9:00 am Tuesday 10/27.

Location

Remote (United States), with core U.S. business hours required.

How you’ll contribute to our mission:

We’re looking for a hands-on AI Engineer to design, build, and ship customer-facing, production-grade features powered by modern LLMs. You’ll partner with product, data, platform, and Customer Experience/Support to turn messy real-world problems into reliable, safe, and measurable AI solutions. You’ll close the loop from voice-of-customer insight → model/design choices → launch → telemetry and iteration, with success measured by outcomes like task completion, CSAT, time-to-resolution, and deflection rate—not just model scores.

You will help develop the next-generation agentic platform that powers customer-facing assistants across the entire journey—from discovery and onboarding to in-product guidance and support. These agents will reason, plan, call internal tools/APIs, retrieve knowledge, and elevate to humans gracefully. You’ll collaborate with CX/Support, Product, and Platform to integrate with CRM and knowledge bases, implement memory and personalization, enforce safety/quality guardrails, and run evaluations and A/B tests. Success is measured in real CX outcomes: shorter time-to-resolution, higher FCR/CSAT, lower effort, and reliable containment.

How you’ll drive success:

• Own end-to-end development of LLM features: problem framing, data prep, prototyping, offline/online evaluation, deployment, and monitoring.
• Build retrieval-augmented generation (RAG) pipelines with vector search (e.g., FAISS, Pinecone, OpenSearch/KNN) and document orchestration.
• Implement prompt strategies, tool use/function calling, and guardrails for safety, bias, and privacy.
• Integrate models in production services (REST/GraphQL/gRPC), including auth, rate limiting, and observability.
• Stand up evals and experiment frameworks (A/B tests, golden sets, regression suites) with clear success metrics.
• Optimize for latency, cost, and quality: prompt compression, caching, model selection, fine-tuning/LoRA, distillation where appropriate.
• Collaborate with DevOps/MLOps/Platform to automate CI/CD, data/version management, and feature flags.
• Embed with CX/Support to mine tickets, chats, and call transcripts; convert VOC into training/eval datasets and backlog priorities.
• Instrument user journeys and define online/offline evals (win rate, hallucination rate, TTR, CSAT/NPS); run A/B tests and ship iterative improvements.
• Build feedback loops (thumbs-up/down, rationale capture, escalation) and human-in-the-loop fallbacks that protect quality.
• Own reliability and UX details that matter for customers: latency budgets, safe fallbacks, clear handoff to human agents, accessibility.
• Partner with Trust/Legal/Security to ensure privacy-by-design and compliant data handling; implement guardrails and red-team mitigations.

Success looks like (first 6 months):

• Document designs and teach best practices to engineering partners.
• Ship 1–2 LLM features to production with SLAs, monitoring, and rollback plans.
• Establish an eval harness (offline + online) and quality gates for prompts/RAG.
• Reduce average latency/cost per request by ≥20% without quality regression.
• Create internal runbooks and dashboards for reproducibility and troubleshooting.

What you bring to help us grow:

• Model customization (fine-tuning/LoRA) and synthetic data generation.
• Streaming and toolcalling/agents, structured outputs (JSON, function schemas).
• Cloud & MLOps: AWS (SageMaker/Bedrock/Lambda), Docker, Terraform, Kubernetes.
• Frontend integration patterns for AI UX (streaming UIs, fallbacks, user feedback loops).
• Domain experience in compliance-heavy environments (e.g., education, finance, healthcare).

What you’ll need to thrive:

• 4–6 years in applied ML/AI or backend engineering with measurable production impact.
• Strong Python and software engineering fundamentals (testing, types, CI/CD).
• Practical LLM experience: OpenAI/Anthropic, or cloud providers (AWS Bedrock, Azure OpenAI, GCP Vertex).
• Experience with at least one deep learning or LLM framework (PyTorch, Transformers, vLLM) and one orchestration library (LangChain, LlamaIndex, Guidance, or custom).
• RAG and data pipelines: chunking/embedding strategies, vector DBs, metadata filtering, and document QA.
• Monitoring/telemetry for AI systems (e.g., MLflow, Weights & Biases, Prometheus, custom eval dashboards).
• Security & privacy awareness (PII handling, redaction, data retention).

Tools you may use:

Python, PyTorch, Hugging Face, vLLM, LangChain/LlamaIndex, FAISS/Pinecone/OpenSearch, Postgres, Redis, Docker, Terraform, GitHub Actions, MLflow/W&B, AWS (Bedrock, SageMaker, Lambda, S3, CloudWatch).

Our Mission, Our People, Our Purpose

At Frontline Education, we’re reimagining what’s possible by becoming an AI-first organization, transforming how we think, work, and serve the educators who shape our schools every day. By using AI in thoughtful, practical ways, we’re creating tools that help educators save time, gain insights, and focus more on that matters most —their students.

How We Support Growth, Balance, and Well-Being

• Personalized Time Off: Take time when it’s needed most — whether that’s a family vacation, a reset day, or simply time to rest and refocus.
• Paid Sick Time: Separate, dedicated sick leave to care for yourself or loved ones.
• Volunteer Time Off: Paid time to give back and support causes that matter to you.
• Ten Paid Holidays: Enjoy meaningful moments and traditions throughout the year.
• Our Philosophy: We believe time away from work helps you bring your best self to it.

Continuous Learning and Growth

• World-Class Learning Access: Explore thousands of on-demand courses through platforms like LinkedIn Learning.
• Leadership & Technical Skill Building: Develop new capabilities and chart your own professional path.
• AI Empowerment: Use OpenAI tools to build fluency with emerging technology and harness AI as a creative partner for innovation and problem-solving.
• Tuition Reimbursement: Invest formal education to advance your skills and career.
• Ongoing Learning Culture: Participate company-led webinars on AI, inclusion, and industry trends—designed to inspire curiosity and continuous improvement.

Health, Happiness, and Purpose

• Wellness Initiatives: Company-sponsored programs that support physical, mental, and emotional well-being.
• Employee Assistance Program (EAP): Confidential support for you and your family’s needs.
• Comprehensive Benefits: Health and financial benefits that support your happiness and future.
• A Culture That Cares: At Frontline Education, we want every team member to learn, grow, and thrive—personally, professionally, and purposefully.

Compensation & Benefits

The full base compensation range for this role is $100,000 - $110,000.

Frontline offers a competitive compensation package designed to reward impact and support your well-being, including:

• Competitive base salary aligned to level, experience, skills, and market data.
• Annual bonus opportunity.
• 401(k) with company match.
• Comprehensive medical, dental, and vision coverage.
• Employee stock purchase opportunities where applicable.
• Tuition reimbursement and professional development support.
• Flexible time off and wellness-focused benefits.

Inclusion, Belonging & Equal Opportunity

Frontline Education is an equal opportunity/affirmative action employer. We aspire to have an inclusive workplace and strongly encourage suitably qualified applicants from a wide range of backgrounds to apply and join our team.

Interview Process & Data Privacy

As part of our interview process, Frontline uses video conferencing tools that include photo capture and may include automated transcription features. A screenshot or photo will be taken at the start of the interview for internal identification and record-keeping purposes only, and transcription may be used to support notetaking and evaluation consistency. These materials are used solely by our recruiting and hiring teams, stored securely, and not shared outside the hiring process. Candidates may opt out of transcription at any time by notifying their recruiter in advance. Frontline processes this information in accordance with applicable data privacy laws and only for legitimate business purposes related to recruitment and hiring.