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 access to 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.
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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.