Sr AI Engineer - AI Solutions

Title: Sr AI Engineer - AI Solutions
Location: 3-4 times a week on site in Victory Park
Duration: Direct Hire
Work Requirements: , Holders or Authorized to Work in the U.S.

Role Overview

The Senior AI Engineer is a senior-level individual contributor responsible for designing, building, and operationalizing enterprise-grade AI solutions within a highly regulated banking environment. This role provides deep technical leadership across AI engineering, MLOps/LLMOps, and governance-by-design, ensuring all AI solutions are secure, scalable, auditable, and production-ready.

You will own complex AI systems end-to-end, influence platform standards, and serve as a technical authority for AI delivery—bridging experimentation and enterprise production while meeting strict risk, privacy, and regulatory requirements.

Key Skills & Expertise

• AI/ML tooling and frameworks
• Agentic AI solutions
• Machine Learning (ML)

Core Technologies

• Cloud Platforms: Azure, Google Cloud Platform
• Data & AI Platforms: Databricks
• Infrastructure as Code: Terraform

Key Responsibilities

1. Technical Leadership & Architecture

• Lead AI engineering initiatives, owning design decisions for complex, high-impact solutions
• Define and contribute to reference architectures, reusable patterns, and “golden paths” for AI development and deployment
• Review and approve solution designs to ensure alignment with platform standards, security controls, and governance requirements

2. Production AI Engineering (Build, Deploy, Run)

• Design and implement production-grade AI services and pipelines (batch and real-time) with a focus on reliability, performance, and operational excellence
• Package and deploy models as scalable services (APIs, jobs, agents) with defined SLAs, monitoring, alerting, and runbooks
• Lead complex issue resolution across environments, including production incidents involving AI systems

3. Governance-by-Design (Regulatory Alignment)

• Embed governance directly into engineering workflows, including:
  • Security and access controls
  • Data classification and handling
  • Model risk management
  • Privacy and consent controls
  • Responsible AI principles
  • Auditability and regulatory traceability
• Partner closely with Risk, Compliance, Legal, and Architecture teams to ensure alignment with internal and external regulatory expectations

4. Generative AI & Advanced Capabilities

• Lead implementation of advanced AI patterns, including Retrieval-Augmented Generation (RAG), embeddings, semantic search, and agent-based workflows
• Ensure GenAI solutions are grounded in approved data sources with governed access, logging, and retention policies
• Define evaluation and monitoring frameworks for GenAI outputs in regulated environments

5. Enterprise MLOps / LLMOps

• Design and implement automated ML/LLM pipelines covering training, evaluation, approval, deployment, and rollback
• Establish standards for model versioning, reproducibility, environment isolation, and controlled releases
• Improve time-to-production while enhancing safety, repeatability, and governance through automation

6. Mentorship & Engineering Excellence

• Mentor engineers and elevate overall technical standards across AI engineering teams
• Contribute to internal best practices, documentation, and knowledge sharing initiatives

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience
• 8+ years of cloud engineering experience, with 5+ years focused on AI/ML systems
• Expert-level proficiency in Python, SQL, and cloud infrastructure
• Hands-on experience deploying AI solutions in Azure and/or Google Cloud Platform environments
• Strong understanding of production systems, including reliability, scalability, observability, cost optimization, and security
• Experience delivering AI solutions in regulated industries (e.g., banking, financial services, insurance, healthcare)
• Familiarity with model risk management, audit requirements, and regulatory review processes
• Hands-on experience with enterprise MLOps/LLMOps tooling and platform design
• Experience designing platform-level AI capabilities, beyond individual model development