T
AI Engineer
Talentify.io
2 hours ago
Full-time
On-site
AI Engineer (Hybrid)
Boston, MA - 3 days onsite / 2 days remote
Role Summary
Responsibilities
Enterprise Workflow Analysis:
Collaborate with corporate functions (Finance, HR, Operations, Safety) and project leaders to identify pain points and AI opportunities that can be standardized across the company.
AI Agent Development:
Build and deploy multiple production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or Python-based frameworks. Integrate agents into Teams/SharePoint on the front end and Databricks Lakehouse or other enterprise data sources on the back end.
RAG Pipelines & LLMOps:
Design and operate retrieval-augmented generation (RAG) pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search (or Spark/Hadoop equivalents). Monitor cost, latency, adoption, and model drift across sites.
Cross-Cloud Engineering:
Implement and maintain integrations across OpenAI, Azure OpenAI, and AWS Bedrock services with secure custom connectors.
Data Integration:
Partner with Data Engineering to deliver ETL/ELT pipelines, APIs, and event-driven connectors that enable enterprise-wide AI solutions.
Adoption & Change Enablement:
Support onboarding and training for both corporate users and field teams, track adoption metrics, and iterate solutions for stronger business impact.
Documentation & Communication:
Produce clear technical documentation, user stories, and specs for AI solutions, while translating outcomes into business value for corporate leadership.
Governance & Compliance:
Ensure all AI solutions meet the company's data governance, security, and compliance requirements.
Qualifications
4+ years in AI engineering, data science, or ML-focused software engineering.
Proven experience building and deploying multiple AI agents in production environments.
2+ years of hands-on experience with LLMs, RAG pipelines, and LLMOps practices.
Strong proficiency in Python, SQL, and Databricks (Spark/Hadoop equivalents acceptable).
Bonus Points
Hands-on experience with Copilot Studio, Power Apps/Automate, API development, and integration.
Familiarity with CI/CD workflows (GitHub Actions, Azure DevOps) and workflow automation.
Solid understanding of ETL/ELT, REST/GraphQL APIs, and enterprise data engineering practices.
Experience working in construction, engineering, or other process-heavy industries.
Advanced technical degree or certifications in AI/ML engineering.
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Responsibilities
Enterprise Workflow Analysis:
Collaborate with corporate functions (Finance, HR, Operations, Safety) and project leaders to identify pain points and AI opportunities that can be standardized across the company.
AI Agent Development:
Build and deploy multiple production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or Python-based frameworks. Integrate agents into Teams/SharePoint on the front end and Databricks Lakehouse or other enterprise data sources on the back end.
RAG Pipelines & LLMOps:
Design and operate retrieval-augmented generation (RAG) pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search (or Spark/Hadoop equivalents). Monitor cost, latency, adoption, and model drift across sites.
Cross-Cloud Engineering:
Implement and maintain integrations across OpenAI, Azure OpenAI, and AWS Bedrock services with secure custom connectors.
Data Integration:
Partner with Data Engineering to deliver ETL/ELT pipelines, APIs, and event-driven connectors that enable enterprise-wide AI solutions.
Adoption & Change Enablement:
Support onboarding and training for both corporate users and field teams, track adoption metrics, and iterate solutions for stronger business impact.
Documentation & Communication:
Produce clear technical documentation, user stories, and specs for AI solutions, while translating outcomes into business value for corporate leadership.
Governance & Compliance:
Ensure all AI solutions meet the company's data governance, security, and compliance requirements.
Qualifications
4+ years in AI engineering, data science, or ML-focused software engineering.
Proven experience building and deploying multiple AI agents in production environments.
2+ years of hands-on experience with LLMs, RAG pipelines, and LLMOps practices.
Strong proficiency in Python, SQL, and Databricks (Spark/Hadoop equivalents acceptable).
Bonus Points
Hands-on experience with Copilot Studio, Power Apps/Automate, API development, and integration.
Familiarity with CI/CD workflows (GitHub Actions, Azure DevOps) and workflow automation.
Solid understanding of ETL/ELT, REST/GraphQL APIs, and enterprise data engineering practices.
Experience working in construction, engineering, or other process-heavy industries.
Advanced technical degree or certifications in AI/ML engineering.
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