AI Engineer

Talentify; Boston (MA); Hybrid; USD 120,000 - 160,000; Full time

Job summary

Talentify seeks an AI Engineer to design, develop, and deploy enterprise AI agents in a hybrid Boston setting. You will collaborate across finance, HR, operations, and safety to standardize AI solutions and deliver measurable business impact.

Responsibilities include building production-ready AI agents, implementing RAG pipelines, and integrating with Teams/SharePoint and Databricks Lakehouse. A strong background in Python, SQL, Databricks, and cross-cloud services is essential.

Qualifications

• 4+ years in AI engineering, data science, or ML-focused software engineering.
• Proven experience building and deploying multiple AI agents in production.
• 2+ years of hands-on experience with LLMs, RAG pipelines, and LLMOps practices.
• Strong proficiency in Python, SQL, and Databricks (Spark/Hadoop equivalents acceptable).

Responsibilities

• Collaborate with corporate functions to identify AI opportunities and standardize across the company.
• Build and deploy production-ready AI agents using Copilot Studio, Power Apps/Automate, ChatGPT Enterprise, or Python-based frameworks.
• Design and operate RAG pipelines with Databricks Delta Tables, Unity Catalog, and Vector Search.
• Implement and maintain integrations across OpenAI, Azure OpenAI, and AWS Bedrock services.
• Partner with Data Engineering to deliver ETL/ELT pipelines, APIs, and event-driven connectors.
• Support onboarding and training for corporate users and field teams, track adoption metrics.
• Produce technical documentation, user stories, and specs for AI solutions.
• Ensure all AI solutions meet data governance, security, and compliance requirements.

Skills

AI engineering

Data science

ML software

Tools

Python

SQL

Databricks

Job description

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.