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About This Role
*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.
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.
- Familiarity with investment management workflows (research, portfolio construction, risk, distribution).
- Experience building internal AI developer platforms or enablement frameworks.
- Knowledge of FinOps practices for AI and data platforms.
- Exposure to knowledge graphs, semantic layers, or enterprise search platforms.
\#LI\-LW1
Compensation Pay Disclosure:
Voya is committed to pay that’s fair and equitable, which means comparable pay for comparable roles and responsibilities.
The below annual base salary range reflects the expected hiring range(s) for this position in the location(s) listed. In addition to base salary, Voya offers incentive opportunities (i.e., annual cash incentives, sales incentives, and/or long\-term incentives) based on the role to reward the achievement of annual performance objectives. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Voya Financial is willing to pay at the time of this posting.
Actual compensation offered may vary from the posted salary range based upon the candidate’s geographic location, work experience, education, licensure requirements and/or skill level and will be finalized at the time of offer. Salaries for part\-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
$180,000\-$190,000Be Well. Stay Well.
Voya provides the resources that can make a difference in your lives. To us, this means thriving physically, financially, socially and emotionally. Voya benefits are designed to help you do just that. That’s why we offer an array of plans, programs, tools and resources with one goal in mind: To help you and your family be well and stay well.
What We Offer
- Health, dental, vision and life insurance plans
- 401(k) Savings plan – with generous company matching contributions (up to 6%)
- Voya Retirement Plan – employer paid cash balance retirement plan (4%)
- Tuition reimbursement up to $5,250/year
- Paid time off – including 20 days paid time off, nine paid company holidays and a flexible Diversity Celebration Day.
- Paid volunteer time — 40 hours per calendar year
Critical Skills
At Voya, we have identified the following critical skills which are key to success in our culture:
- Customer Focused: Passionate drive to delight our customers and offer unique solutions that deliver on their expectations.
- Critical Thinking: Thoughtful process of analyzing data and problem solving data to reach a well\-reasoned solution.
- Team Mentality: Partnering effectively to drive our culture and execute on our common goals.
- Business Acumen: Appreciation and understanding of the financial services industry in order to make sound business decisions.
- Learning Agility: Openness to new ways of thinking and acquiring new skills to retain a competitive advantage.
Equal Employment Opportunity
*Voya Financial is an equal\-opportunity employer. Voya Financial provides equal opportunity to qualified individuals regardless of race, color, sex, national origin, citizenship status, religion, age, disability, veteran status, creed, marital status, sexual orientation, gender identity, genetic information, or any other status protected by state or local law.*
Reasonable Accommodations
*Voya is committed to the inclusion of all qualified individuals. As part of this commitment, Voya will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment,* *please* *reference* *resources for applicants with disabilities**.*
Salary Context
This $180K-$190K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Voya Financial, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. Disclosed range: $180K to $190K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Voya Financial AI Hiring
Voya Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $190K - $190K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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