Member of Technical Staff – Applied AI Engineering

NTT DATA AIVista

Applied AI engineer building governed agents and production infrastructure for AIVista’s regulated enterprise customers. Customizing foundation models, deploying workflows, and ensuring reliable, auditable operation.

Posted 8/4/2026full-timeSan Francisco • California, Washington • 🇺🇸 United StatesLead💰 $300,000 - $400,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and deploying AI products, with a strong focus on production-quality software engineering in Python and modern languages. Proven ability to customize foundation models and manage enterprise-scale workflows while ensuring compliance and reliability.

Highest-signal resume keywords

AI Product DevelopmentProduction-Quality Software EngineeringAWS DeploymentContainer OrchestrationModel Customization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonGoTypeScriptRustRAGLLM-Powered ApplicationsAgent FrameworksModel APIsProduction SystemsSoftware Engineering

Soft Skills

Clear CommunicationAutonomyProblem-Solving

Tools & Technologies

AWSAzureGCPDockerKubernetesHelmLangGraphLlamaIndexOpenAIAmazon Bedrock

Industry Keywords

Agentic SystemsEnterprise AIProduction ReadinessGovernanceObservability

Tech Stack

Tools & technologies

AWSAzureDockerGoGoogle Cloud PlatformKubernetesPythonRustTypeScript

About the role

Key responsibilities & impact

Design and deploy agentic systems, infrastructure, and workflows for enterprise AI products
Customize foundation models to customer domains using tuning, RAG, and related techniques
Build scalable, reliable software systems for production enterprise workloads
Deploy capabilities alongside customers and partner with cross-functional and customer teams
Improve capabilities, troubleshoot technical issues, and turn customer feedback into product improvements
Ship agents that run regulated back-office processes using systems of record, policies, risk classifications, and human approval steps
Build domain-specialization layers for foundation models and validate them on real customer cases
Design orchestration, tool integration, memory, and context infrastructure for reliable agent operation
Establish evaluation and guardrail harnesses for production readiness
Instrument governance and observability so agent decisions are auditable and policy-aligned
Reduce latency and cost of enterprise-scale agentic workflows without sacrificing reliability

Requirements

What you’ll need

8+ years building and shipping AI products, including production systems
Bachelor’s degree in a technical field, or equivalent combination of education, training, and experience
Production-quality software engineering in Python and another modern language such as Go, TypeScript, or Rust
Track record of taking systems from zero to production
Experience taking AI capabilities from prototype to production, including agentic workflows, RAG, or model customization
Hands-on experience deploying and operating services on AWS, Azure, or GCP
Experience with containers and orchestration such as Docker, Kubernetes, or Helm
Experience building LLM-powered and agentic applications using model APIs such as OpenAI, Anthropic, or Amazon Bedrock
Experience with agent frameworks such as LangGraph or LlamaIndex
Experience with retrieval-augmented generation (RAG)
Ability to work with high autonomy and ambiguity while maintaining rigor
Clear communication skills and comfort working with enterprise customers and cross-functional teams

Benefits

Comp & perks

Medical, dental, and vision insurance
401(k) plan
Significant company HSA contribution
Paid holidays and flexible PTO
Annual target bonus and other cash incentives, with a combined potential of up to 100% of base salary