Edtech.com's Summary

Frontline Education is hiring a Senior AI Engineer to design, develop, and deploy production-grade AI features leveraging large language models to enhance customer experience and product performance. The role involves full lifecycle ownership from problem framing and data preparation to deployment, monitoring, and iteration, with a focus on measurable customer experience outcomes and collaboration across multiple departments.

Highlights

Own end-to-end development of LLM-powered customer-facing features, including prototyping, deployment, and monitoring.
Build retrieval-augmented generation (RAG) pipelines using vector search tools like FAISS, Pinecone, and OpenSearch.
Implement prompt strategies, tool use, safety guardrails, and responsible AI practices.
Integrate AI models with production services using REST, GraphQL, or gRPC with full observability and security.
Run evaluations, A/B tests, and build feedback loops to continuously improve AI-driven experiences.
Required skills include Python, PyTorch, Hugging Face, LangChain, AWS (SageMaker, Bedrock), Docker, Terraform, MLflow, and other MLOps tools.
Qualifications: 4-6 years of applied ML/AI or backend engineering experience, strong software engineering skills, practical hands-on LLM knowledge, and familiarity with compliance-heavy environments.
Collaborate closely with Customer Experience, Product, Development, and Trust/Legal teams to ensure safety, privacy, and quality in AI solutions.
Compensation includes base salary, bonus structure, 401(k) match, Employee Stock Purchase Plan, tuition reimbursement, and personalized PTO.
Company specializes in K-12 school administration software, providing innovative solutions to educational institutions.

Senior AI Engineer Full Description

Senior AI Engineer

Location: Wayne, PA, United States

Description

Sr. AI Engineer

Location Requirements: This position can sit remotely (anywhere in the US) or hybrid to Wayne, PA or Naperville, IL. The role will work hours based on the Eastern Time Zone.

This position reports to the Sr. Director of CX-AI Engineering and partners with Customer Experience, Development, Architecture, Product, and Strategic partners to design and deliver services that strengthen customer satisfaction and optimize product performance.

Your role on the team:

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 escalate 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.

You can expect to:

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 the role:

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).

This role requires:

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).

Who we are:

Frontline Education is a pioneer of school administration software purpose-built for K-12 districts. We provide innovative, connected solutions for student and special programs, business operations, and human capital management with powerful data and analytics to empower educators and administrators. We earn the trust of K-12 leaders across the U.S. by serving as a consistently high-performing, forthright partner of school districts through every dimension of the company.

We're a group of unique and talented individuals that love what we do. We've been lucky enough to land jobs with a rapidly growing tech company that supports an appreciative and friendly customer base. We work hard to make our customers happy, but we like to have a good time in the process. We are a company that strives to think in terms of “we” instead of “me.” We believe in the philosophy of servant leadership and that it’s all about putting others first. We also value the balance between family and work.

Frontline embraces diversity, equity, and inclusivity. We are intentionally building a workplace that respects, supports, and values the identities of all our employees. We believe this to be foundational in developing a strong community in our company. Frontline Education is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

The perks of being a Frontliner:

Frontline offers a competitive compensation package including a base salary, rewarding bonus structure, 401k match, ESPP, and personalized PTO! Our company growth has created a promising environment for career advancement and rewarding challenges. We offer a tuition reimbursement program for eligible college credit coursework available to employees depending on their status and length of employment.

Original Job Description

Senior AI Engineer

Location: Wayne, PA, United States

Description

Sr. AI Engineer

Location Requirements: This position can sit remotely (anywhere in the US) or hybrid to Wayne, PA or Naperville, IL. The role will work hours based on the Eastern Time Zone.

Your role on the team:

You can expect to:

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 the role:

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).

This role requires:

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).

Who we are:

The perks of being a Frontliner:

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