Ai Engineer Jobs

About The Position

Requirements

• Strong proficiency in Python, SQL, VS Code, GitHub Copilot, and data integration tools (e.g., SQL, Pyspark, experience with cloud platforms (Azure, AWS, GCP) and container architecture.
• Hands-on experience building LLM / GenAI applications production grade AI patterns like MCP, Agentic AI, RAG, NLP2SQL etc.
• Solid SQL and PostgreSQL skills; experience with PGVector or a comparable vector database (Pinecone, Neo4j etc.).
• Comfort designing and consuming REST APIs; understanding of authentication, rate limiting, and error handling.
• Working knowledge of at least one major model provider API—Anthropic (Claude) and/or OpenAI (GPT)—including prompt design, function/tool calling, and streaming.
• Experience with AI/ML frameworks and AI concepts.
• Familiarity with financial data providers (e.g., Bloomberg, Refinitiv, Factset etc.).
• Deep understanding of and asset management domain, data governance, regulatory compliance, and risk management in banking.
• Excellent communication and stakeholder management skills.
• Understanding of SQL, Oracle, Exadata, Snowflake, Data Bricks, etc..
• Understanding of development Languages: Python , .NET/C#, React, and Java
• Understanding of operating systems: Unix/BASH Shell, Windows, Linux OS
• Proficient in generative AI and LLM data preparation for financial use cases.
• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• 10+ years of experience in data engineering or integration, preferably in financial services or banking.

Responsibilities

• Lead development of a portfolio of client-facing AI capabilities and integration methods, embedding them into client front ends and interactive dashboards to ensure these capabilities are well aligned and meet required validation and responsible AI standards.
• Develop and oversee business unit wise approach to integrate structured and unstructured data into NT’s enterprise AI framework in collaboration with the AI architecture, AI engineering, and data platform engineering teams.
• Develop and lead the practice of data pipeline engineering, rationalizing custom data integrations over time to establish common methods and approaches.
• Architect and implement data pipelines that integrate structured and unstructured data from internal banking systems, external feeds, and cloud platforms for AI/ML use cases.
• Drive our semantic architecture and engineering approach for Northern Trust intelligence to advance enterprise context engineering and architecture disciplines.
• Collaborate with the AI consulting team, business units, data scientists, model risk teams, and other stakeholders to understand data requirements for AI models supporting key use cases such as portfolio management, quants & research, reconciliations, and customer intelligence; drive engineering specifications and delivery for these capabilities.
• Ensure sound practices are executed to deliver and maintain metadata management, data lineage, and audit trails for AI data assets as development and data teams expand AI data access.
• Provide leadership to multiple Technical Delivery teams to prioritize and refine critical data deliverables based on business use cases and high-value data assessments.
• Work closely on vendor negotiations and guide data solutions with procurement and transformation teams to enable scalable consumption with favorable and appropriate terms and capabilities, including understanding support or vendor SLA requirements.
• Provide guidance for regulatory compliance processes, audits, and internal reviews.
• Act as End-to-End Data Integration Product Management Vision and Roadmap leader in support of AI transformation programs.
• Support real-time and batch data ingestion from asset management vendor platforms i.e. Aladdin, Eagle, iCapital, and third-party APIs.
• Optimize data workflows for performance, reliability, and cost-efficiency across hybrid cloud environments.
• Partner with cybersecurity and compliance teams to ensure data privacy, encryption, and access controls are enforced.
• Contribute to the development of enterprise-wide AI architecture standards and governance frameworks, ensuring secure and efficient data consumption.
• Provide indirect leadership across lateral teams of data analysts focused on improving data accountability, access control, and user enablement.
• Provide mentoring and coaching to senior, mid-level, and junior engineers, product managers, and architects; function as a team lead as required as the AI and AI data integration practice is scaled.

Benefits

• retirement benefits (401k and pension)
• health and welfare benefits (medical, dental, vision, spending accounts and disability)
• paid time off
• parental and caregiver leave
• life & accident insurance
• other voluntary and well-being benefits
• discretionary bonus program that may include an equity component