Director, AI Engineering & Agentic Platform
Together we fight for everyone's opportunity for a better financial future.
We will do this together — with customers, partners and colleagues. We will fight for others, not against: We will stand up for and champion everyone's accessto opportunities. The status quo is not good enough … we believe every individual and every community deserves access to financial opportunities. We are determined to support both individuals and communities in reaching a better financial future. We know that reaching this future depends on our actions today.
Like our Purpose Statement, Voya believes in being bold and committed to action.We are committed to a work environment where the differences that we are born with — and those we acquire throughout our lives — are understood, valued and intentionally pursued.We believe that our employees own our culture and have a responsibility to foster an environment where we all feel comfortable bringing our whole selves to work.
Purposefully bringing our differences together to positively influence our culture, serve our clients and enrich our communities is essential to our vision.
Are you ready to join a company with a strong purpose and a winning culture? Start your Voyage – Apply Now
Role Overview
At Voya Investment Management, we are committed to building innovative, responsible, and scalable technology solutions that enable better investment outcomes for our clients. Our vision for AI is grounded in delivering secure, governed, and high-impact capabilities that augment investment decision-making, improve operational efficiency, and enhance client engagement.
Get to Know the Opportunity
As a Director, AI Engineering & Agentic Platform, you will be responsible for designing, building, and operating the AI engineering capabilities. This role is a builder-operator hybrid, focused on delivering production-grade AI systems — not research prototypes — that can be trusted and scaled across investment research, distribution, and operational functions.
You will lead the development of shared AI platform services, including LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and agentic workflows, enabling multiple data science and engineering teams to deliver use cases faster, with stronger governance and reliability.
This role requires a combination of deep technical expertise in LLMOps and AI system architecture, platform thinking, and strong leadership in enterprise environments, particularly within the context of financial services where security, compliance, and trust are critical.
The Contributions You'll Make
AI Platform Architecture & Engineering
- Design and implement scalable AI architectures, including:
- LLM-powered applications
- Retrieval-Augmented Generation (RAG) systems
- agentic / multi-step workflows
- vector search and retrieval services
- model serving and inference layers
- Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
- Define reference architectures and engineering standards for production AI systems.
LLMOps / MLOps Enablement
- Build and operationalize AI delivery pipelines:
- CI/CD for models, prompts, and workflows
- prompt versioning and lifecycle management
- evaluation and testing frameworks
- model and artifact registries
- Implement monitoring for:
- response quality and hallucination control
- latency, throughput, and system reliability
- cost observability and optimization
- Establish scalable experimentation and evaluation frameworks to measure AI performance and reliability.
Responsible AI, Governance, and Security
- Design AI systems with strong controls for:
- data security and privacy
- auditability and traceability
- entitlements and access controls
- data lineage and governance
- Partner with risk, compliance, and security teams to embed Responsible AI practices into development and deployment processes.
- Ensure alignment with regulatory expectations and model risk management standards.
Engineering Execution & Operational Excellence
- Lead delivery of production-grade AI systems with a focus on:
- scalability and reliability
- latency and performance optimization
- operational readiness and support
- Evaluate and integrate third-party AI platforms and tools where appropriate.
- Drive cost-effective architecture and FinOps practices for AI workloads.
Data Platform Integration
- Partner closely with data engineering and platform teams to integrate AI capabilities with:
- Snowflake and Databricks environments
- structured and unstructured data pipelines
- APIs and enterprise data services
- semantic and knowledge-layer architectures
- Enable seamless access to governed datasets for AI applications.
Leadership & Stakeholder Management
- Serve as a technical leader and advisor to senior stakeholders across business and technology teams.
- Translate business needs into scalable AI platform capabilities and solutions.
- Lead and mentor a team of AI / ML engineers and technical leads.
- Drive adoption of AI capabilities through enablement, best practices, and reusable frameworks.
Minimum Knowledge and Experience
- Bachelor's degree in Computer Science, Engineering, or related field.
- 10+ years of experience in software engineering, ML engineering, or platform engineering.
- 3+ years in a leadership role driving complex engineering initiatives or leading teams.
AI Engineering & Architecture
- Hands-on experience designing and deploying:
- LLM-based applications
- RAG systems
- agentic AI workflows
- vector databases / semantic search solutions
- Strong understanding of prompt engineering patterns and evaluation methodologies.
- Experience with model serving, inference optimization, and production deployment.
ML Engineering / Platform Mindset
- Strong background in building scalable, production-grade systems with focus on:
- reliability and observability
- latency and performance
- cost optimization
- Experience developing shared platforms or reusable services across multiple teams.
LLMOps / MLOps
- Experience implementing:
- CI/CD pipelines for ML / AI systems
- model and artifact registries
- evaluation and regression pipelines
- monitoring and alerting frameworks
- Familiarity with prompt lifecycle management and AI system governance controls.
Data Platform & Cloud Technologies
- Strong experience with modern data / AI platforms, including:
- Databricks and/or Snowflake
- APIs and microservices architectures
- unstructured data processing pipelines
- semantic layer or knowledge graph concepts
Enterprise & Financial Services Context
- Experience working in regulated environments with strong requirements for:
- security and data privacy
- governance and auditability
- SDLC and change management processes
- Financial services or investment management experience strongly preferred.
Soft Skills
- Excellent communication and stakeholder management skills.
- Ability to influence technical and non-technical audiences.
- Strong problem-solving and strategic thinking capabilities.
Nice to Have
- Experience with Azure AI services, Copilot Studio, or similar enterprise AI tools.
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Director, AI Engineering & Agentic Platform
Together we fight for everyone's opportunity for a better financial future.
We will do this together — with customers, partners and colleagues. We will fight for others, not against: We will stand up for and champion everyone's accessto opportunities. The status quo is not good enough … we believe every individual and every community deserves access to financial opportunities. We are determined to support both individuals and communities in reaching a better financial future. We know that reaching this future depends on our actions today.
Like our Purpose Statement, Voya believes in being bold and committed to action.We are committed to a work environment where the differences that we are born with — and those we acquire throughout our lives — are understood, valued and intentionally pursued.We believe that our employees own our culture and have a responsibility to foster an environment where we all feel comfortable bringing our whole selves to work.
Purposefully bringing our differences together to positively influence our culture, serve our clients and enrich our communities is essential to our vision.
Are you ready to join a company with a strong purpose and a winning culture? Start your Voyage – Apply Now
Role Overview
At Voya Investment Management, we are committed to building innovative, responsible, and scalable technology solutions that enable better investment outcomes for our clients. Our vision for AI is grounded in delivering secure, governed, and high-impact capabilities that augment investment decision-making, improve operational efficiency, and enhance client engagement.
Get to Know the Opportunity
As a Director, AI Engineering & Agentic Platform, you will be responsible for designing, building, and operating the AI engineering capabilities. This role is a builder-operator hybrid, focused on delivering production-grade AI systems — not research prototypes — that can be trusted and scaled across investment research, distribution, and operational functions.
You will lead the development of shared AI platform services, including LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and agentic workflows, enabling multiple data science and engineering teams to deliver use cases faster, with stronger governance and reliability.
This role requires a combination of deep technical expertise in LLMOps and AI system architecture, platform thinking, and strong leadership in enterprise environments, particularly within the context of financial services where security, compliance, and trust are critical.
The Contributions You'll Make
AI Platform Architecture & Engineering
- Design and implement scalable AI architectures, including:
- LLM-powered applications
- Retrieval-Augmented Generation (RAG) systems
- agentic / multi-step workflows
- vector search and retrieval services
- model serving and inference layers
- Establish reusable platform services, APIs, and design patterns to accelerate delivery across multiple teams.
- Define reference architectures and engineering standards for production AI systems.
LLMOps / MLOps Enablement
- Build and operationalize AI delivery pipelines:
- CI/CD for models, prompts, and workflows
- prompt versioning and lifecycle management
- evaluation and testing frameworks
- model and artifact registries
- Implement monitoring for:
- response quality and hallucination control
- latency, throughput, and system reliability
- cost observability and optimization
- Establish scalable experimentation and evaluation frameworks to measure AI performance and reliability.
Responsible AI, Governance, and Security
- Design AI systems with strong controls for:
- data security and privacy
- auditability and traceability
- entitlements and access controls
- data lineage and governance
- Partner with risk, compliance, and security teams to embed Responsible AI practices into development and deployment processes.
- Ensure alignment with regulatory expectations and model risk management standards.
Engineering Execution & Operational Excellence
- Lead delivery of production-grade AI systems with a focus on:
- scalability and reliability
- latency and performance optimization
- operational readiness and support
- Evaluate and integrate third-party AI platforms and tools where appropriate.
- Drive cost-effective architecture and FinOps practices for AI workloads.
Data Platform Integration
- Partner closely with data engineering and platform teams to integrate AI capabilities with:
- Snowflake and Databricks environments
- structured and unstructured data pipelines
- APIs and enterprise data services
- semantic and knowledge-layer architectures
- Enable seamless access to governed datasets for AI applications.
Leadership & Stakeholder Management
- Serve as a technical leader and advisor to senior stakeholders across business and technology teams.
- Translate business needs into scalable AI platform capabilities and solutions.
- Lead and mentor a team of AI / ML engineers and technical leads.
- Drive adoption of AI capabilities through enablement, best practices, and reusable frameworks.
Minimum Knowledge and Experience
- Bachelor's degree in Computer Science, Engineering, or related field.
- 10+ years of experience in software engineering, ML engineering, or platform engineering.
- 3+ years in a leadership role driving complex engineering initiatives or leading teams.
AI Engineering & Architecture
- Hands-on experience designing and deploying:
- LLM-based applications
- RAG systems
- agentic AI workflows
- vector databases / semantic search solutions
- Strong understanding of prompt engineering patterns and evaluation methodologies.
- Experience with model serving, inference optimization, and production deployment.
ML Engineering / Platform Mindset
- Strong background in building scalable, production-grade systems with focus on:
- reliability and observability
- latency and performance
- cost optimization
- Experience developing shared platforms or reusable services across multiple teams.
LLMOps / MLOps
- Experience implementing:
- CI/CD pipelines for ML / AI systems
- model and artifact registries
- evaluation and regression pipelines
- monitoring and alerting frameworks
- Familiarity with prompt lifecycle management and AI system governance controls.
Data Platform & Cloud Technologies
- Strong experience with modern data / AI platforms, including:
- Databricks and/or Snowflake
- APIs and microservices architectures
- unstructured data processing pipelines
- semantic layer or knowledge graph concepts
Enterprise & Financial Services Context
- Experience working in regulated environments with strong requirements for:
- security and data privacy
- governance and auditability
- SDLC and change management processes
- Financial services or investment management experience strongly preferred.
Soft Skills
- Excellent communication and stakeholder management skills.
- Ability to influence technical and non-technical audiences.
- Strong problem-solving and strategic thinking capabilities.
Nice to Have
- Experience with Azure AI services, Copilot Studio, or similar enterprise AI tools.
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