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About This Role
SS&C is a leading provider of mission-critical, AI-powered technology and services empowering financial services and healthcare organizations to work smarter, faster, and securely. Founded in 1986, SS&C is headquartered in Windsor, Connecticut, and has offices worldwide. More than 23,000 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
Senior AI Platform Engineer
Locations : San Francisco, CA / Jacksonville, FL / Boston, MA / New York, 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 looking for Senior AI Platform Engineers to join our R&D team. These engineers will design and implement the core AI framework, ensuring it seamlessly integrates with our big data infrastructure, and enabling scalable, intelligent, and autonomous data-driven systems.
Why You Will Love It Here!
- Flexibility : Hybrid Work Model & a Business Casual Dress Code, including jeans
- Your Future: 401 (k) 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
- Design and build a scalable Agentic AI framework on top of our data platform.
- Implement MCP- and A2A-based architectures for agent collaboration and orchestration.
- Integrate AI services with data pipelines (Airflow, Spark, Trino, StarRocks, DBT).
- Build APIs, SDKs, and tooling to enable other teams to use the framework.
- Work with platform and application teams to ensure secure, reliable, and multi-tenant deployment of AI agents.
- Evaluate new agentic AI, LLM, and orchestration technologies and recommend adoption.
- Mentor other engineers and contribute to AI engineering best practices .
What You Will Bring
Core Skills (Must-Have)
- Computer Science/Engineer related Bachelor’s degree
- Strong programming skills in Python with 5+ years of experience
- Proven experience with data engineering or distributed systems (Spark, Hive, Trino, StarRocks, DBT, Airflow, Kubernetes).
- Deep understanding of LLM-based systems and agent frameworks (LangChain, LlamaIndex, DSPy, OpenAI MCP, A2A).
- Hands-on experience with API design & integration (REST, GraphQL, gRPC).
- Knowledge of cloud infrastructure & container orchestration (Kubernetes, Docker).
- Strong understanding of IAM, security, and multi-tenancy in enterprise systems.
- Track record of building production-grade AI/data platforms .
Desired Skills (Nice-to-Have)
- Experience in Information Security .
- Experience with vector databases (pgvector, Pinecone, Weaviate, Milvus, Vespa).
- Familiarity with event-driven architectures (Kafka, Pulsar).
- Understanding of retrieval-augmented generation (RAG) patterns.
- Background in authorization/identity systems (ReBAC, RBAC, Zanzibar-style).
- Familiarity with observability (OpenTelemetry, DataHub, MLflow, Prometheus).
- Experience in enterprise AI governance (audit, lineage, compliance).
- Contributions to open-source AI frameworks (LangChain, OpenAI MCP, Hugging Face, etc.).
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-RK
#CA-RK
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. California: The expected base salary for the position is between 150,000 USD to 180,000 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 $150K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($165K) sits 22% below the category median. Disclosed range: $150K to $180K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
SS&C AI Hiring
SS&C has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US. Compensation range: $180K - $180K.
Location Context
AI roles in San Francisco pay a median of $261,000 across 2,646 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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