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Gen AI Engr. 2 (Conversational AI & Customer Support)

Roles & Responsibilities

  • 2+ years hands-on experience building and deploying LLM-based systems in production
  • Deep familiarity with RAG architectures, including embedding models, vector databases, retrieval strategies, and response grounding; experience with evaluation/benchmarking and mitigation of hallucinations
  • Proficiency in Python and experience with LLM APIs (OpenAI, Anthropic, open-source) plus prompt engineering, prompt chaining, or agent orchestration frameworks
  • Experience building and maintaining ML/data pipelines in AWS or similar cloud infrastructure (e.g., Lambda, S3, RDS) and integrating LLM features with internal systems

Requirements:

  • Design, build, and continuously improve end-to-end LLM-powered systems across product and operations, including internal tools and customer-facing features
  • Own end-to-end RAG pipelines: document ingestion, chunking, embeddings, retrieval tuning, and response synthesis
  • Develop and enforce guardrails, grounding strategies, and confidence thresholds to mitigate hallucination and ensure output reliability
  • Architect robust, maintainable, and cost-effective prompt chains and agent workflows; design evaluation frameworks and monitor production performance

Job description

Job Title: Gen AI Engineer 2 (Conversational AI & Customer Support)

Location: Remote

Employment Type: Full-Time


About Us:

Amira Learning accelerates literacy outcomes by delivering the latest reading and neuroscience with AI. As the leader in third-generation edtech, Amira listens to students read out loud, assesses mastery, helps teachers supplement instruction and delivers 1:1 tutoring. Validated by independent university and SEA efficacy research, Amira is the only AI literacy platform proven to achieve gains surpassing 1:1 human tutoring, consistently delivering effect sizes over 0.4.


Rooted in over thirty years of research, Amira is the first, foremost, and only proven Intelligent Assistant for teachers and AI Reading Tutor for students. The platform serves as a school district’s Intelligent Growth Engine, driving instructional coherence by unifying assessment, instruction, and tutoring around the chosen curriculum.


Unlike any other edtech tool, Amira continuously identifies each student’s skill gaps and collaborates with teachers to build lesson plans aligned with district curricula, pulling directly from the district’s high-quality instructional materials. Teachers can finally differentiate instruction with evidence and ease, and students get the 1:1 practice they specifically need, whether they are excelling or working below grade level. 


Trusted by more than 2,000 districts and working in partnership with twelve state education agencies, Amira is helping 3.5 million students worldwide become motivated and masterful readers. 


Job Summary:

A self-motivated A-player who is results-oriented, operates at a fast pace, and takes pride in delivering high-quality, trustworthy AI systems; an experienced generative AI practitioner with deep expertise in LLM-based system design, including prompt engineering, RAG architectures, fine-tuning, and evaluation; highly focused on accuracy and reliability, with a clear understanding that hallucinations and misinformation erode user trust and equipped with concrete strategies to prevent them; proficient in building and maintaining end-to-end production ML pipelines, from data preparation through deployment and monitoring; and a strong collaborator who works effectively across engineering, product, and customer-facing teams in a fully remote environment.


Essential Functions:

Generative AI System Design & Development

  • Design, build, and continuously improve LLM-powered systems across Amira's product and operations — from internal tools to customer-facing features

  • Own RAG pipelines end-to-end: document ingestion, chunking strategy, embedding selection, retrieval tuning, and response synthesis

  • Develop and enforce guardrails, grounding strategies, and confidence thresholds to mitigate hallucination and ensure output reliability

  • Architect prompt chains and agent workflows that are robust, maintainable, and cost-effective at scale

Fine-Tuning, Evaluation & Continuous Improvement

  • Design and operate evaluation frameworks to measure system accuracy, helpfulness, hallucination rate, and task completion across generative AI features

  • Fine-tune and adapt foundation models for domain-specific tasks, including data curation, training pipeline setup, and performance benchmarking

  • Implement automated and human-in-the-loop review processes to catch and correct problematic outputs

  • Monitor production traffic, identify failure modes, and iterate rapidly on retrieval, prompting, and generation strategies

Integration & Infrastructure

  • Integrate LLM-powered features with internal systems and third-party platforms (e.g., Salesforce, CRM tools) via APIs, connectors, and data sync workflows

  • Contribute to shared ML infrastructure and tooling used across Amira's AI systems

  • Help explore and implement solutions that make generative AI economically viable within the budget constraints typical of public schools and education SaaS

Cross-Functional Collaboration

  • Partner with learning design, content, product, and customer success teams to ensure AI systems are grounded in accurate, up-to-date domain knowledge

  • Translate business needs into well-scoped generative AI solutions and communicate tradeoffs clearly to non-technical stakeholders


Qualifications (Education and Experience):

  • 2+ years of hands-on experience building and deploying LLM-based systems in production

  • Deep familiarity with RAG architectures: embedding models, vector databases, retrieval strategies, and response grounding

  • Demonstrated experience with evaluation and benchmarking of LLM outputs — including hallucination mitigation, confidence filtering, output validation, and fallback strategies

  • Practical experience with prompt engineering, prompt chaining, and/or agent orchestration frameworks (LangChain, LlamaIndex, or similar)

  • Proficiency in Python and experience working with LLM APIs (open-source, Anthropic, OpenAI, etc.)

  • Experience building and maintaining ML or data pipelines in AWS or similar cloud infrastructure (Lambda, S3, RDS, etc.)

  • Degree in computer science or a related technical field, or equivalent practical experience

Preferred Qualifications:

  • Experience fine-tuning foundation models or running RLHF / preference-based feedback loops for domain-specific improvement

  • Experience in education SaaS or with education-sector customers (districts, schools, state agencies)

  • Familiarity with Salesforce or similar CRM platforms and their API/data ecosystems

  • Experience with evaluation tooling, custom eval harnesses, or LLM-as-judge approaches

  • Background working with conversational AI, chatbots, or customer-facing generative AI features

  • Proven ability to operate in a fast-paced, goal-oriented startup environment and manage multiple concurrent workstreams


Benefits:

  • Competitive Salary

  • Medical, dental, and vision benefits

  • 401(k) with company matching

  • Flexible time off

  • Stock option ownership

  • Cutting-edge work

  • The opportunity to help children around the world reach their full potential


Commitment to Diversity:

Amira Learning serves a diverse group of students and educators across the United States and internationally. We believe every student should have access to a high-quality education and that it takes a diverse group of people with a wide range of experiences to develop and deliver a product that meets that goal. We are proud to be an equal opportunity employer.

The posted salary range reflects the minimum and maximum base salary the company reasonably expects to pay for this role. Salary ranges are determined by role, level, and location. Individual pay is based on location, job-related skills, experience, and relevant education or training. We are an equal opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, sex, sexual orientation, gender identity or expression, age, disability, medical condition, pregnancy, genetic information, marital status, military service, or any other status protected by law.

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