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SS&C

Cloud-based SaaS for financial operations

Director of Engineering AI/Data Science - Agentic AI platform

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeWaltham, MA, USANo salary listed

About the Job

Requirements

  • Expert Python skills, including object-oriented programming, asynchronous programming, testing, and packaging, and hands-on experience with agentic libraries including LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, and BeeAI.
  • Familiarity with JavaScript or TypeScript for edge agents or user-interface integrations is a plus.
  • Strong knowledge of large language models, including GPT-4o, Claude, Llama 3, and domain-specific models, and fine-tuning techniques.
  • Understanding of embeddings, vector similarity search using approximate nearest neighbors, and retrieval-augmented generation pipelines; experience with OpenAI, Anthropic, Hugging Face, and Ollama.
  • Proficiency with vector databases such as Pinecone, Milvus, and Qdrant; graph databases such as Neo4j and Amazon Neptune; and traditional SQL or NoSQL systems.
  • Ability to design schemas and queries for agent context management.
  • Experience building extract, transform, and load pipelines with Airflow, NiFi, or Spark to populate and maintain knowledge bases.
  • Skills in containerization with Docker, orchestration with Kubernetes and AWS EKS or GKE, serverless computing with AWS Lambda or Cloud Run, continuous integration and continuous delivery with GitHub Actions or Jenkins, and infrastructure as code.
  • Familiarity with cloud machine-learning platforms such as AWS Bedrock, GCP Vertex AI, or Azure ML for scalable agent hosting.
  • Knowledge of secure coding practices, identity and access management, role-based access control, data encryption, and AI governance, including prompt-injection mitigation, bias auditing, and compliance documentation.
  • Strong problem-solving, communication, and collaboration skills; ability to translate business requirements into technical specifications; and intellectual curiosity and adaptability.
  • Master’s degree or PhD, or equivalent experience, in Data Science, Information Technology, Applied Mathematics, Engineering, Computer Science, or a related field.
  • Five to seven years of data science and/or software engineering experience in artificial intelligence and machine learning, with a proven record of building and deploying AI models.
  • Twelve or more years of experience leading, managing, and developing teams.
  • Strong technical background and deep understanding of machine learning, with practical experience building and implementing large-scale predictive models and recommendation systems.
  • Proficiency in AI frameworks such as PyTorch and programming languages such as Python, with experience building and deploying AI models for end-to-end AI/ML solutions.
  • Proven leadership experience in data analytics and AI, or a related role, with a strong background in designing and implementing data science solutions.
  • Prior experience leading technical engagements across data engineering, data science, AI, and generative AI workstreams.
  • Strong leadership and proactive communication to coordinate with project teams and internal stakeholders.
  • Experience delivering solutions on major cloud platforms, data science tools, and generative AI technologies.
  • Strong financial technology business acumen, with experience developing proofs of concept and bringing them to product vision.
  • Ability to guide decisions on leading-edge technical and business approaches and/or develop major new technical tools.

Responsibilities

  • 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 delivery of AI/ML and large-language-model-based solutions from concept through production, ensuring scalability, security, and maintainability.
  • Establish software engineering best practices for AI development, including continuous integration and delivery, 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 execution discipline across teams, including sprint planning, milestone tracking, and delivery accountability.
  • Guide teams in building production-ready solutions using modern AI technologies, including large language models, vector databases, retrieval-augmented generation, agent frameworks, APIs, microservices, and 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.
  • Facilitate communication between executives, staff, management, vendors, and other technology resources within and outside the organization.

Desired Qualifications

  • Familiarity with JavaScript or TypeScript for edge agents or user-interface integrations.

About the Company

SS&C

Cloud-based SaaS for financial operations

SS&C Technologies provides cloud-based software and services for financial services firms, focusing on investment and asset management. Its subscription-based SaaS tools automate back-office tasks, portfolio and investment management, and related workflows, all hosted in the cloud. The platform integrates with other financial systems to manage assets, processing, reporting, and compliance from a single place, helping reduce manual work and costs. By offering breadth across wealth, asset, and property management with a services-enabled SaaS model, its goal is to automate, standardize, and optimize back-office operations so firms can focus on managing investments and client relationships.

Company size
10,001+
Company stage
IPO
Headquarters
Windsor, CT
Founded
1986

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