AI/ML Engineer

Reporting to the Director of Data & AI Engineering, the AI/ML Engineer is a mid-level, hands-on technical role responsible for building and maintaining the data pipelines, AI models, and intelligent features that power the GLOBO platform. This role spans the full lifecycle of AI development-from cleaning and preparing data, to building and evaluating models, to shipping production features that directly improve operational efficiency and customer experience.

The AI/ML Engineer works across GLOBO’s modern data stack (Fivetran, dbt, Snowflake) and AI infrastructure (AWS Bedrock, LLMs, agentic frameworks) to deliver reliable, well-tested solutions. This person is equally comfortable wrangling messy data and prompt-engineering an LLM, and takes pride in writing clean, tested code that other engineers can build on.

Data Engineering & Pipeline Development:

• Build and maintain reliable ingestion pipelines usingSnowflake Openflow, Python, and Snowflake, including API, PostgreSQL, and CDC-based integrations.
• Develop incremental synchronization, cursor/state management, retry logic, schema-drift handling, soft-delete propagation, and source-to-target reconciliation.
• Transform raw source data through staging, intermediate, and core models into trusted datasets for analytics, reporting, and machine-learning workloads.
• Apply data-quality checks for freshness, completeness, uniqueness, referential integrity, valid relationships, and business-rule compliance.
• Maintain source definitions, model documentation, lineage, metadata, and data contracts.
• Collaborate with data owners to ensure PII/PHI classification, masking, retention, and deletion requirements are implemented throughout the pipeline.

Model Development, Testing & Evaluation:

• Implement monitoring and alerting for ingestion failures, pipeline freshness, schema changes, data-quality failures, transformation errors, model drift, and inference degradation.
• Establish automated regression testing for dbt models, features, evaluation datasets, prompts, and model outputs.
• Validate that sensitive data is appropriately masked, redacted, access-controlled, and excluded from unauthorized model training or data-sharing workflows.
• Build safeguards for PII/PHI in recorded-call, transcript, and AI/ML processing pipelines, including verification that redaction and deletion workflows complete successfully.
• Ensure AI/ML outputs are traceable to their source data, model or prompt version, feature set, and evaluation results.
• Define recovery procedures, data-quality escalation paths, and operational runbooks for critical pipelines and models.
• Support human review and approval for model outputs that may affect customers, interpreters, employees, financial activity, or service quality.

Feature Development & Integration:

• Collaborate with Product and Engineering to ship AI-powered features into the GLOBO platform.
• Build and deploy LLM integrations (AWS Bedrock, Anthropic Claude) and agentic workflows (CrewAI, LangChain).
• Write production-quality code with proper tests, documentation, and error handling.

Reliability & Safety:

• Implement guardrails, monitoring, and alerting for AI services in production.
• Ensure AI outputs are consistent and trustworthy.
• Contribute to evaluation datasets, prompt versioning, and regression testing for deployed models.

Performance & Cost Optimization:

• Monitor and optimizeSnowflake compute, storage, query performance, dbt execution, Openflow runtime usage, and model-inference costs.
• Design efficient incremental models, CDC pipelines, materializations, clustering strategies, and warehouse/task schedules.
• Compare and optimize ingestion costs as Globo transitions from Fivetran to Snowflake Openflow.
• Reduce unnecessary full refreshes, duplicate processing, excessive data movement, and inefficient feature recomputation.
• Optimize model selection, prompt size, token usage, batching, caching, inference frequency, and routing between model providers.
• Measure model performance against operational cost, latency, throughput, and data-freshness requirements.
• Establish practical service-level targets for critical datasets, transformations, batch jobs, and model-serving workflows.

Required Minimum Education and Experience:

• Bachelor’s Degree in Computer Science, Data Science, Information Systems, or related field.
• 2+ years of experience in data engineering, software development, or ML engineering.
• Experience with the below tech stack is required:
• Python (advanced proficiency)
• SQL (advanced proficiency)
• LLM Integration (AWS Bedrock, Anthropic Claude, or OpenAI API)
• dbt (data transformation and testing)
• Snowflake (or similar cloud data warehouse)
• AWS Lambda / Serverless architecture
• Experience with the below tech stack is preferred:
• Fivetran (or similar ELT/ingestion tooling)
• Agentic Frameworks (CrewAI, LangChain, or similar)
• Airflow (or similar workflow orchestration)
• Vector Databases (Pinecone, PGVector, or OpenSearch)
• AWS ECS/EKS
• CDK and CloudFormation for automated deployments
• Ruby on Rails (ability to read/debug core platform code)
• Redis
• PostgreSQL
• React
• Familiarity with model evaluation techniques, prompt engineering, and AI safety best practices.
• Experience with Google Docs and Apple/Mac Operating System preferred

Additional Preferred Requirements:

• Ability to work independently in a decentralized environment without the reliance on direct authority
• Highest level of personal and professional integrity and ethics
• Broad understanding of current and emerging technology practices
• High level of initiative, accountability, and follow-through
• Value strong teamwork and collaboration skills
• Demonstrated problem-solving and decision-making skills
• Ability to manage multiple initiatives and projects and prioritize needs
• Strong sense of service and passion for the company and business
• Authorized to legally work for any employer in the United States
• Willingness to submit to any requested background checks
• Fluent in English

About GLOBO:GLOBO is a B2B communication platform provider, specializing in translation and interpretation technology, services, data, and insights. For the third year in a row, GLOBO has been ranked in the top-10 on the Philadelphia 100 list of fastest-growing privately held companies.What’s it like to work here? We’re a close-knit team with big ideas and ambitions. We make the impossible happen, and make hard tasks easier. We don’t take ourselves too seriously, but we’re serious about our mission-helping people communicate when it matters most.