[Hiring] Senior Data & Applied AI Engineer @almentor

Role Description

Reporting to the Head of Engineering, your role is to design, build, and operate the data and applied AI foundations that power intelligent learning experiences and business workflows. You will ship production-grade pipelines, retrieval/search systems, and LLM-enabled features that are measurable, secure, and reliable.

We are committed to building software that is not only functional and reliable, but also beautiful and intuitive. Our engineers and designers work together to create products that are a joy to use, and that solve real-world problems in innovative ways. We believe in the Agile and XP methodologies, and we prioritize collaboration, communication, and continuous improvement in everything we do.

We are embarking on an exciting journey of rebuilding our product and engineering organization to drive innovation and growth. Our goal is to create a world-class platform that delivers the best possible learning experiences to our users.

As a Senior Data / AI Engineer, your days will blend data engineering, applied AI implementation, and operational ownership. You'll spend your time on:

• Architecting, implementing, and maintaining pipelines that ingest, transform, and serve data for product features, analytics, and AI workflows.
• Building indexing and retrieval layers (structured + unstructured) to power search, recommendations, and AI assistants (RAG).
• Implementing LLM-powered features: prompt orchestration, tool/function calling, RAG patterns, structured extraction, summarization.
• Adding guardrails: policy checks, rate limits, cost controls, and safe handling of sensitive data.
• Creating evaluation harnesses and regression tests for retrieval of quality and AI outputs (offline datasets + automated checks).
• Instrumenting pipelines/services with logging, tracing, metrics, and data quality monitoring.
• Partnering closely with product managers, designers, and engineers to translate user needs into measurable data/AI capabilities.
• Participating in sprint planning, refinement, and cross-team alignment to keep execution predictable.
• Owning your pipelines and services in production — incident response, post-mortems, runbooks, and continuous improvement.
• Optimizing latency, cost, and throughput across data processing, indexing, and AI inference usage.

A typical day might look like: checking pipeline freshness and alerts in the morning, tuning chunking/index mappings for a new course search mid-day, pairing with product on evaluation metrics for an AI study Assistant in the afternoon, and ending the day by shipping a monitored RAG endpoint with cost budgets and regression tests.

Qualifications

• 4+ years of experience in data engineering, backend engineering, or platform engineering, with meaningful production ownership.
• Strong SQL and data modeling skills; experience designing schemas and data contracts.
• Experience building and operating pipelines (batch and/or streaming), including backfills and data quality validation.
• Experience with search/indexing systems and retrieval patterns (e.g., OpenSearch/Elasticsearch, vector DBs, hybrid search).
• Hands-on experience implementing LLM-powered features (RAG, tool/function calling, prompt/version management) — no deep ML required.
• Familiarity with CI/CD, containerization (Docker), and cloud services (ex. AWS, GCP).
• Strong problem-solving and debugging skills, with a proactive attitude towards owning and improving systems.
• Strong written and verbal communication skills, with the ability to explain complex ideas clearly to technical and non-technical stakeholders.
• Bachelor's degree in computer science, Engineering, or a related field is preferred but not required.

Requirements

• Design, oversee, build and operate data pipelines and applied AI systems that power our education platform across consumer and business customer segments.
• Build and scale retrieval and search foundations (hybrid search, metadata filtering, vector retrieval) that enable AI experiences.
• Deliver production LLM capabilities with measurable quality, cost controls, and safety guardrails.
• Improve system performance, reliability, observability, and maintainability through proactive monitoring and data quality checks.
• Collaborate closely with stakeholders (product managers, designers, frontend/mobile engineers) to translate business requirements into technical solutions.
• Contribute to sprint planning, technical documentation, and cross-team initiatives as a senior engineering voice.