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About This Role
As a leading financial services and healthcare technology company based on revenue, SS\&C is headquartered in Windsor, Connecticut, and has 27,000\+ employees in 35 countries. Some 20,000 financial services and healthcare organizations, from the world's largest companies to small and mid\-market firms, rely on SS\&C for expertise, scale, and technology.
Job Description
Director, AI Platform Engineering
Locations : San Francisco, CA / Boston, MA / Waltham, MA / NY, NY \| Hybrid
Get To Know Us:
SS\&C Advent's financial solutions are going Agentic! And we're growing the team to get us there. Our best\-of\-breed products that span the investment firm's front\-to\-back office will be part of the journey to digital transformation, which started with our cloud\-native, award\-winning Genesis Platform used by clients around the globe.
We are building a next\-generation Agentic AI Framework that integrates Model Context Protocol (MCP), Agent\-to\-Agent (A2A) communication, Machine Learning, and advanced orchestration on top of our enterprise big data platform.
We are seeking a Director of AI Platform Engineering to lead the design, development, and scaling of our enterprise AI agent platform. This role owns the technical vision and execution for a production\-grade, multi\-tenant platform that enables the organization to build, deploy, and operate AI agent workflows at scale — across investment management, data analytics, and operational domains.
You will lead a team of platform engineers, architect critical infrastructure, and drive the strategy for multi\-agent orchestration, LLM integration, observability, and developer experience.
Why You Will Love It Here!
- Flexibility : Hybrid Work Model and Business Casual Dress Code, including jeans
- Your Future: 401k Matching Program, Professional Development Reimbursement
- Work/Life Balance: Flexible Personal/Vacation Time Off, Sick Leave, Paid Holidays
- Your Wellbeing: Medical, Dental, Vision, Employee Assistance Program, Parental Leave
- Wide Ranging Perspectives: Committed to Celebrating the Variety of Backgrounds, Talents and Experiences of Our Employees
- Training: Hands\-On, Team\-Customized, including SS\&C University
- Extra Perks: Discounts on fitness clubs, travel and more!
What You Will Get To Do:
Platform Strategy \& Architecture
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- Define and execute the technical roadmap for the AI agent platform (multi\-agent orchestration, LLM routing, tool integration, observability)
- Architect for enterprise\-grade multi\-tenancy, security, and scalability
- Drive platform standardization across agent frameworks (LangGraph, MCP protocol, AG\-UI protocol)
- Evaluate and integrate emerging AI technologies (new LLM providers, inference optimization, agentic frameworks)
Team Leadership
-------------------
- Build, lead, and mentor a team of 8\-15 platform engineers (backend, infrastructure, AI/ML)
- Establish engineering best practices: code review, testing, CI/CD, documentation
- Create a culture of technical excellence, ownership, continuous learning, prototyping and delivering
- Foster cross\-functional collaboration with product, data science, and business stakeholders
Delivery \& Operations
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- Own platform reliability, availability, and performance SLAs
- Drive production readiness: observability, alerting, incident response, capacity planning
- Deliver iterative platform capabilities through discovery\-driven development processes
- Manage dependencies across multiple consuming teams and agent developers
Technical Decision Making
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- Lead architectural decisions using structured frameworks (DACI, ADR etc)
- Evaluate build vs. buy vs. integrate trade\-offs for platform components
- Champion developer experience: APIs, SDKs, documentation, self\-service tooling
- Ensure AI agent platform security, compliance, and audit requirements
What You Will Bring:
- 10\+ years in software engineering, with 5\+ years in engineering leadership and management roles (managing managers or large teams)
- Deep experience building and operating production platforms (API platforms, data platforms, or ML platforms)
- Experience with multi\-agent orchestration frameworks (LangGraph, CrewAI, AutoGen)
- Strong knowledge with Model Context Protocol (MCP) or similar tool\-integration standards
- Hands\-on expertise with modern AI/ML ecosystem: LLM APIs (OpenAI, Anthropic, AWS Bedrock), agent frameworks, prompt engineering
- Strong systems design: distributed systems, microservices, event\-driven architecture
- Production cloud native infrastructure at scale (Kubernetes, Docker, Terraform)
- Python as primary backend language (FastAPI, async patterns)
- Proven track record of shipping platform products used by multiple internal or external teams
- Experience leading technical strategy at the organizational level (not just team\-level)
Nice\-to\-Have Qualifications:
- Background in financial services or regulated industries (compliance, audit trails, data governance)
- Experience with real\-time streaming protocols (SSE, WebSocket, AG\-UI)
- Knowledge of data mesh / data platform architectures (GraphQL, dbt, data catalogs)
- Exposure to Module Federation / micro\-frontend architectures
- Open source contributions or community leadership in AI/ML space
Experience scaling from 0* 1 platform through growth stages
Thank you for your interest in SS\&C! If applicable, to further explore this opportunity, please apply directly with us through our Careers page on our corporate website: www.ssctech.com/careers .
\#LI\-MB3
\#CA\-MB
Unless explicitly requested or approached by SS\&C Technologies, Inc. or any of its affiliated companies, the company will not accept unsolicited resumes from headhunters, recruitment agencies, or fee\-based recruitment services.
SS\&C Technologies offers a comprehensive total rewards package designed to support your wellbeing, growth, and future. Our benefits include medical, dental, and vision coverage; a 401(k) plan with company match; paid time off, holidays, and parental leave; and professional development reimbursement opportunity.
Actual base salary will vary based on several factors, including but not limited to relevant skills, prior experience, education, demonstrated performance, and geographic location.
New York: The expected base salary for the position is between 200000 USD to 230000 USD. California: The expected base salary for the position is between 200000 USD to 230000 USD. Other States expected base salary for the position is between 200000 USD to 230000 USD. Massachusetts: The expected base salary for the position is between 200000 USD to 230000 USD.
In addition, employees in this role may be eligible for consideration on an annual basis for a discretionary bonus and/or equity awards, such as restricted stock units or stock options, based upon individual and business performance at the company’s discretion.###
Applications will be accepted on an ongoing basis until the position is filled.
SS\&C Technologies is an Equal Employment Opportunity employer and does not discriminate against any applicant for employment or employee on the basis of race, color, religious creed, gender, age, marital status, sexual orientation, national origin, disability, veteran status or any other classification protected by applicable discrimination laws.
Salary Context
This $200K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SS&C, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $200K to $230K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
SS&C AI Hiring
SS&C has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span TX, US, San Francisco, CA, US. Compensation range: $110K - $230K.
Location Context
AI roles in San Francisco pay a median of $265,000 across 1,335 tracked positions. That's 23% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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