AI Engineer

Amalgamated Bank of NY•New York, NY

8h•$105,000 - $120,000•Hybrid

About The Position

The AI Engineer develops, tests, deploys, and supports enterprise AI solutions that advance Amalgamated Bank’s Data & AI strategy. Reporting to the Head of Data & AI and working closely with the Lead AI Engineer, this role helps design and implement AI agents, large language model applications, retrieval solutions, integrations, and governance controls using Databricks and approved enterprise AI platforms. The position spans data engineering, software development, AI governance, and business process optimization. It offers the opportunity to build expertise in generative AI, agentic architectures, and responsible AI practices in a regulated financial services environment.

Requirements

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Information Systems, or a related field.
1–3 years of professional experience in software engineering, data engineering, machine learning, artificial intelligence, or a related technical discipline.
Proficiency in Python and SQL.
Experience working with cloud-based data platforms, preferably Databricks or similar technologies.
Foundational knowledge of large language models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and AI application development.
Strong analytical, problem-solving, and troubleshooting abilities.
Effective verbal and written communication skills, including the ability to document technical solutions clearly.

Nice To Haves

Experience with Databricks, Azure, Git, CI/CD pipelines, and API development.
Familiarity with agentic AI frameworks such as LangGraph, LlamaIndex, Semantic Kernel, or comparable technologies.
Exposure to AI governance, model evaluation methodologies, and responsible AI practices.
Experience supporting dashboards, reporting solutions, or operational monitoring capabilities.
Understanding of AI and Data Governance principles.
Prior experience within financial services, banking, or another regulated industry.

Responsibilities

Design, develop, test, and support AI-powered applications, copilots, and agent-based solutions using Python, Databricks, and approved enterprise AI platforms.
Assist in building retrieval-augmented generation (RAG) solutions, workflow automations, and multi-agent architectures.
Develop and maintain integrations between AI solutions and enterprise systems, databases, APIs, and business applications.
Participate in prompt engineering, context management, tool integration, and agent orchestration.
Support evaluation frameworks, test datasets, and benchmarking methodologies for measuring AI performance.
Help monitor model quality, accuracy, safety, latency, and cost efficiency using dashboards and operational metrics.
Troubleshoot AI application issues and contribute to performance tuning and continuous improvement.
Participate in validation activities to ensure AI solutions meet business, regulatory, and governance requirements.
Prepare, transform, and validate structured and unstructured data for AI and analytical applications.
Collaborate with Data Engineering and Data Governance teams to use trusted, certified, and well-governed data sources.
Support development and maintenance of vectorized knowledge repositories and retrieval services.
Contribute to data lineage, metadata, and documentation efforts supporting audit and governance requirements.
Follow standards and policies for Responsible AI, information security, privacy, model governance, change management, access controls, data handling, and model lifecycle management.
Support documentation and evidence collection for model reviews, audits, and regulatory examinations.
Help monitor AI safety, accuracy, traceability, observability, and operational effectiveness guardrails.
Partner with data engineers, BI engineers, analysts, and business stakeholders to deliver practical AI solutions.
Support dashboards and operational reporting that measure AI adoption, effectiveness, and business value.
Research emerging AI technologies, tools, and engineering practices and share findings with the Data & AI team.
Participate in peer reviews, technical discussions, and knowledge-sharing activities.
Perform other duties as directed.