Senior AI / MLOps Engineer – e-Commerce Search & Information Retrieval at Algolia

Seattle, Washington, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

e-Commerce, Technology, AI SearchIndustries

Requirements

Spend 1-2 days per week in a local coworking space to collaborate with your teammates in person
5+ years of experience in software engineering

Responsibilities

Productionization & Packaging: Convert notebooks and research codebase into production-ready Python and Go micro-services, libraries, or kubeflow pipelines, and design reproducible build pipelines (Docker, Conda, Poetry) and manage artefacts in centralized registries
Scalable Deployment: Orchestrate real-time and batch inference workloads on Kubernetes, AWS/GCP managed services, or similar platforms, ensuring low latency and high throughput, and Implement blue-green / canary rollouts, automatic rollback, and model versioning strategies (SageMaker, Vertex AI, KServe, MLflow, BentoML, etc.)
MLOps & CI/CD: Build and maintain CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins, Argo) covering unit, integration, data-quality, and performance tests, and Automate feature store updates, model retraining triggers, and scheduled batch jobs using Airflow, Dagster, or similar orchestration tools
Observability & Reliability: Define and monitor SLIs/SLOs for model latency, throughput, accuracy, drift, and cost, and Integrate logging, tracing, and metrics (Datadog etc.) and establish alerting & on-call practices
Data & Feature Engineering: Collaborate with data engineers to create scalable pipelines that ingest clickstream logs, catalog metadata, images, and user signals, and Implement real-time and offline feature extraction, validation, and lineage tracking
Performance & Cost Optimization: Profile models and services; leverage hardware acceleration (GPU, TPU), libraries (ONNX, OpenVINO), and caching strategies (Redis, Faiss) to meet aggressive latency targets, and Right-size clusters and workloads to balance performance with cloud spend
Governance & Compliance: Embed security, privacy, and responsible-AI checks in pipelines; manage secrets, IAM roles, and data-access controls via Terraform or CloudFormation, and Ensure auditability and reproducibility through comprehensive documentation and artifact tracking
Collaboration & Mentorship: Partner closely with Data Scientists, Product Owners, and Site Reliability Engineers to align technical solutions with business goals, and Coach junior engineers on MLOps best practices and contribute to internal knowledge-sharing sessions

Skills

Key technologies and capabilities for this role

AIMLOpsPythonGoKubernetesDockerKubeflowAWSGCPMicroservicesInformation RetrievalMachine LearningSearch RankingRecommendationsCondaPoetry

Questions & Answers

Common questions about this position

What are the main responsibilities of this Senior AI / MLOps Engineer role?

You'll handle productionization of ML prototypes into Python and Go micro-services, scalable deployment on Kubernetes and AWS/GCP, MLOps with CI/CD pipelines, observability with SLIs/SLOs, and data/feature engineering for e-commerce search.

What technical skills and tools are required for this position?

Key skills include Python, Go, Kubernetes, Docker, CI/CD tools like GitHub Actions and Argo, ML platforms like SageMaker and MLflow, orchestration with Airflow, and observability tools like Datadog.

Is this role remote or does it require office work?

This information is not specified in the job description.

What is the salary or compensation for this position?

This information is not specified in the job description.

What kind of experience makes a strong candidate for this role?

Strong candidates have expertise in MLOps, productionizing ML models into scalable services, Kubernetes deployment, CI/CD automation, and observability for AI systems in high-scale environments like e-commerce search.

Algolia

Provides hosted search and discovery solutions

About Algolia

Algolia offers search and discovery solutions through a hosted search API, enabling businesses to create fast and relevant search experiences. Its services are particularly useful for e-commerce platforms and media companies, providing extensive support to help clients integrate advanced search capabilities. Algolia operates on a subscription model, allowing clients to access its APIs without managing infrastructure, which ensures scalability and reliability. The company aims to enhance user experience by enabling tailored and efficient search functionalities for businesses of all sizes.

Palo Alto, CaliforniaHeadquarters

2012Year Founded

$324.8MTotal Funding

SERIES_DCompany Stage

Consumer Software, Enterprise SoftwareIndustries

501-1,000Employees

Benefits

Flexible Work Hours

Remote Work Options

Risks

AWS's own search solutions could challenge Algolia's market position.

Pricing pressures from the PAYGO model may reduce profit margins.

Dependency on commercetools could impact Algolia if service disruptions occur.

Differentiation

Algolia offers a unique end-to-end AI Search and Discovery platform.

The company provides flexible, usage-based pricing through AWS Marketplace.

Algolia's integration with commercetools enhances its composable commerce capabilities.

Upsides

Algolia's AI Personalization tool meets growing demand for personalized e-commerce experiences.

The Pay-As-You-Go model expands Algolia's reach in the AWS ecosystem.

Recognition as a leader in Gartner's Magic Quadrant boosts Algolia's market credibility.

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