Senior AI/ML Engineer

TIAA

. Design and implement enterprise-grade generative AI solutions and comprehensive pipelines .

Posted 10/2/2026full-timeUnited StatesSenior💰 $150,000 - $209,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing enterprise-grade generative AI solutions, including building and optimizing production-grade AI agents and machine learning pipelines. Proficient in AWS cloud-native application development, CI/CD practices, and security controls.

Highest-signal resume keywords

Generative AI SolutionsAWS Cloud-Native DevelopmentPython ProgrammingMachine Learning PipelinesCI/CD Pipeline Implementation

ATS Keywords

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Hard Skills

Machine LearningStatistical ModelingFeature EngineeringModel MonitoringData PreprocessingReinforcement LearningGraph DatabasesLow-Code DevelopmentHigh-Code DevelopmentPrompt Engineering

Soft Skills

Technical LeadershipCollaborationDocumentation

Tools & Technologies

DockerKubernetesTerraformGitHub ActionsJenkinsGitLab CIAWS CodePipelineEKSECSVector Databases

Industry Keywords

AI/ML ServicesMicroservices ArchitectureEvent-Driven DesignDistributed SystemsMulti-Cloud Architectures

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsDockerGoogle Cloud PlatformJenkinsKubernetesMicroservicesPythonRustSDLCTerraformC++Go

About the role

Key responsibilities & impact

Design and implement enterprise-grade generative AI solutions and comprehensive pipelines
Build end-to-end systems integrating structured and unstructured data stores, graph databases, indexing strategies, reinforcement learning strategies, embedding models, and LLMs
Build and optimize production-grade AI agents using low-code and high-code implementations
Design, train, and deploy machine learning pipelines covering data ingestion, feature engineering, model selection, hyperparameter tuning, and validation
Apply statistical modeling and exploratory data analysis to complex datasets
Design feature stores and data preprocessing and transformation workflows
Implement model monitoring for concept drift, data quality degradation, and performance regression
Architect and develop large-scale cloud-native AWS Python applications for high performance, low latency, and horizontal scalability
Implement Terraform Infrastructure as Code and comprehensive unit, integration, end-to-end, performance, and AI-specific testing
Apply security controls including IAM, least privilege, RBAC, MFA, encryption, key management, and data masking/tokenization
Design and manage Docker containers with Kubernetes/EKS or ECS orchestration and auto-scaling
Establish CI/CD practices using GitHub Actions, Jenkins, GitLab CI, or AWS CodePipeline
Maintain documentation of architectures, data flows, security controls, and operational procedures
Contribute throughout the full SDLC from requirements gathering and architecture design through deployment and optimization

Requirements

What you’ll need

5+ years of software engineering experience with demonstrated progression in technical leadership and system design
3+ years of hands-on AI/ML experience, including at least 1+ year focused on Generative AI, LLMs, and production deployment
Extensive AWS experience with compute, storage, networking, security, and AI/ML services
Expert-level Python programming, including advanced language features, design patterns, and performance optimization
Full-stack development skills, including RESTful backend API development and frontend development
Production experience with LLM APIs, RAG frameworks, vector databases, prompt engineering, AI agent frameworks, model fine-tuning, and evaluation
Experience building CI/CD pipelines using Terraform or CloudFormation, Docker, Kubernetes/EKS, and monitoring and observability tools
Understanding of distributed systems, microservices architecture, event-driven design, and scalability patterns
University degree preferred
Preferred: Azure or GCP and multi-cloud architectures; open-source AI/ML contributions or published research; C++, Go, or Rust; real-time streaming; multimodal models, vision transformers, or diffusion models

Benefits

Comp & perks

Superior retirement program
Competitive health, wellness, and work life offerings
Financial, emotional, and physical well-being support
Future-focused skills and AI tools
Meaningful learning experiences and development pathways
Performance-linked incentive program may be included