Director of Engineering - Agentic AI platform

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 of Engineering AI/Data Science

Location(s): Waltham, MA

Get To Know the Team

You'll be joining a collaborative, fast-moving team of data scientists, AI engineers, machine learning engineers, and data engineers who work together to tackle complex, high-impact problems at the intersection of AI and enterprise software.

We operate with a genuinely agile mindset — shipping iteratively, challenging assumptions, and staying close to the cutting edge. The team is proactive about research, consistently evaluating and adopting state-of-the-art methodologies, and we encourage everyone to experiment, share findings, and bring new ideas to the table. If you thrive in an environment where intellectual curiosity is the norm and the work is always evolving, you'll fit right in.

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

• Lead and manage multiple engineering teams focused on AI, Data Science, and platform development.
• Drive the design and implementation of Agentic AI frameworks, including orchestration, tool use, memory, workflow automation, and multi-agent systems.
• Oversee the delivery of AI/ML and LLM-based solutions from concept through production, ensuring scalability, security, and maintainability.
• Establish software engineering best practices for AI development, including CI/CD, testing, observability, model lifecycle management, and performance monitoring.
• Partner with Product Management to define roadmaps, prioritize use cases, and ensure timely delivery of high-impact AI capabilities.
• Collaborate with architecture, cloud, and platform teams to build reusable AI services and enterprise-ready frameworks.
• Ensure strong execution discipline across teams, including sprint planning, milestone tracking, and delivery accountability.
• Guide teams in building production-ready solutions using modern AI technologies (LLMs, vector databases, RAG, agent frameworks, APIs, microservices, cloud platforms).
• Promote responsible AI practices, including governance, data privacy, model evaluation, and risk controls.
• Mentor engineering managers, technical leads, and senior engineers to build a high-performing organization.
• Drive cross-functional collaboration with Product, UX, Data, Security, and Infrastructure teams.
• Communicate progress, risks, and outcomes to senior leadership and stakeholders
• Identify opportunities to standardize platforms, reduce duplication, and accelerate delivery through shared frameworks.

What You Will Bring

• Programming & Frameworks: Expert Python skills (OOP, async, testing, packaging) and hands-on experience with agentic libraries including LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, and BeeAI. Familiarity with JavaScript/TypeScript (for edge agents or UI integrations) is a plus.
• LLMs & Machine Learning: Strong knowledge of LLMs (GPT-4o, Claude, Llama 3, domain-specific models) and fine-tuning techniques. Understanding of embeddings, vector similarity search (ANN), and RAG pipelines. Experience with providers such as OpenAI, Anthropic, Hugging Face, and Ollama.
• Databases & Data Engineering: Proficiency with vector databases (Pinecone, Milvus, Qdrant), graph databases (Neo4j, Amazon Neptune), and traditional SQL/NoSQL systems. Ability to design schemas and queries for agent context management. Experience building ETL pipelines (Airflow, NiFi, Spark) to populate and maintain knowledge bases.