AI/ML Engineer

Veritis Group Inc; Chicago (IL); On-site; USD 140,000 - 210,000; Full time

Job summary

Veritis Group Inc. seeks an AI/ML Engineer to design, develop, deploy, monitor, and optimize enterprise AI/ML solutions, including generative AI, LLMs, and RAG architectures, working with stakeholders and CoE.

You'll maintain ML ops, governance, and ensure secure, compliant operation across cloud-native platforms such as AWS, Databricks, and vector databases, with strong Python and cloud skills.

Qualifications

• Bachelor’s degree or equivalent work experience in a related field.
• 7+ years of relevant experience in AI/ML.
• Experience delivering ML/AI solutions in production.
• Experience with ML lifecycle: development, deployment, monitoring, governance.

Responsibilities

• Design, develop, deploy, monitor, and optimize enterprise AI/ML solutions.
• Partner with stakeholders, Data Engineering, Security, Risk, Compliance, Architecture and CoE.
• Establish ML Ops and AI Ops practices; ensure governance and responsible AI controls.
• Evaluate emerging AI technologies and drive adoption for business outcomes.

Skills

Python programming

ML/DL concepts

Gen AI, LLMs

RAG architectures

AI Ops / MLOps

Cloud platforms

CI/CD

Linux

Education

Bachelor’s degree in Computer Science/IT/Data Science/Engineering/Math

Tools

AWS SageMaker

Kubernetes

Databricks

TensorFlow/PyTorch

Job description

The AI/ML Engineer will be responsible for designing, developing, deploying, monitoring, and optimizing enterprise AI and machine learning solutions. This includes traditional machine learning models, Generative AI solutions, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) architectures, AI agents, and agentic workflows. The role will partner closely with business stakeholders, Data Engineering, Information Security, Risk Management, Compliance, Architecture, and the AI/ML Center of Excellence (CoE) to identify, prioritize, and deliver AI-enabled business capabilities.

This role will maintain and enhance AI/ML infrastructure, establish ML Ops and AI Ops practices, implement governance and responsible AI controls, and ensure AI solutions operate securely, reliably, transparently, and in compliance with organizational and regulatory requirements. The AI/ML Engineer will support technologies and platforms such as Microsoft Copilot, Github Copilot, AWS Bedrock, Databricks, Python, machine learning frameworks, model lifecycle tools, AI orchestration frameworks, vector databases, and other emerging AI technologies.

The AI/ML Engineer will evaluate emerging technologies and identify opportunities to use AI to improve operational efficiency, decision-making, customer experience, and business outcomes across the Bank.

Education & Experience

• Bachelor’s degree in computer science, Information Technology, Data Science, Engineering, Mathematics, or a related field, or equivalent work experience
• Minimum 7 years of relevant work experience
• Experience working within an Agile/SCRUM environment
• Experience delivering and supporting machine learning and AI solutions in production environments
• Experience with the machine learning lifecycle, including development, deployment, monitoring, governance, and retirement
• Experience with Generative AI, Large Language Models (LLMs), and foundation models
• Experience building AI-powered applications and services
• Experience implementing Retrieval-Augmented Generation (RAG) solutions
• Experience designing and implementing AI agents and agentic workflows with responsible AI and human oversight controls
• Experience with machine learning libraries and frameworks
• Experience with AI orchestration frameworks
• Experience with vector databases, embeddings, and semantic search technologies
• Experience with ML Ops, AI Ops, and model lifecycle management frameworks
• Experience working with databases, data lakes, and modern data platforms
• Experience building cloud-native applications, services, CI/CD pipelines, and Infrastructure as Code (IaC)
• Experience with cloud computing platforms, specifically Amazon Web Services technologies such as SageMaker, Bedrock, Docker, Lambda, and Kubernetes
• Experience troubleshooting production applications, AI systems, and cloud infrastructure
• Experience integrating AI solutions with enterprise systems, APIs, and third-party platforms
• Strong Python programming experience

Knowledge & Abilities

• An ability to adopt new ways of working and embrace new technologies and techniques
• Dynamic communication skills with a focus on building relationships by listening and asking questions
• Extensive and effective presentation skills
• Strong leadership and team building skills
• Ability to establish and communicate priorities, constraints, deadlines, and goals
• Ability to develop strong trust relationships with stakeholders
• Strong organizational skills and experience with formal SDLC
• Ability to solve complex problems and think analytically
• Highly self-motivated and directed
• Ability to absorb and retain information quickly
• Ability to present technical concepts in user-friendly language
• Possess an agile mindset and openness to adaptation based on experience and feedback
• Strong attention to detail and a focus on long-term strategic quality
• Promote a culture of collaboration, innovation, and continuous improvement
• Consistently participate in diversity, equity and inclusion (DEI) events and use a DEI perspective to enhance the Bank’s culture and positively impact our business, members, and communities
• Strong understanding of machine learning, deep learning, Generative AI, LLMs, embeddings, vector search, prompt engineering, and RAG
• Understanding of AI agent architecture, tool use, multi-agent patterns, and workflow orchestration
• Understanding of Responsible AI principles, model governance, AI risk management, security, privacy, and regulatory considerations
• Ability to design AI solutions that balance innovation and business value with security, compliance, human oversight, and operational reliability
• Ability to evaluate AI, ML, and agent performance using quantitative and qualitative methods
• Ability to troubleshoot model drift, hallucinations, unintended agent behavior, performance degradation, and operational issues
• Linux and Windows systems administration