Senior Director, Software Engineering - Data and AI Platforms

At Epiq , your work contributes to complex, global legal outcomes. You’ll join a values‑driven community where integrity guides decisions, relentless service sets the bar, and we thrive on big challenges together. We invest in your growth with enterprise‑wide learning and mobility. We celebrate who you are, and we respect life beyond work with flexibility that’s recognized externally. Enabled by modern platforms and AI, you’ll do the most meaningful work of your career and see your impact at scale.

Job Description: Role Description Epiq, the world’s leading Legal Solutions provider, is changing the legal tech game. We are looking for a Senior Director, Software Engineering – Data & AI Platforms to expand data and information management capabilities across our organization. The successful candidate will help deliver our vision for our Data & AI Platforms, working directly under the CTO, together with Product Management, Operations, AI/ML, and other stakeholders in the organization. The person will be a thought leader driving the Software Development function along with the Data Integration/Data Quality initiatives of the Epiq Service Cloud, including the data foundations that power our AI and GenAI capabilities, and a tremendous team player who builds connections across the organization.

This is still a hands-on role, so we are looking for someone who can not only set our vision to advance this capability but also help implement it, leading a team of 80- 100 people based primarily in India. The team is using a cloud-native, modern technology stack built on AWS, Microsoft Azure, Snowflake, Kubernetes, and PowerBI, with growing investment in streaming, Lakehouse, and GenAI/vector infrastructure, among other things. This is a leadership role and requires working in our office premises in a hybrid model.

Responsibilities

• Demonstrated hands-on software engineering background (e.g., Python, Scala, or Java), with a track record of personally writing, reviewing, and shipping production-grade code, not solely directing the work of others.
• Solid grounding in core engineering practices: version control (Git), CI/CD pipelines, automated testing, and formal code review standards.
• Experience designing and building APIs, services, and event-driven/ microservices architectures that other engineering and product teams build on top of.
• Working knowledge of infrastructure-as-code (e.g., Terraform, CloudFormation) and containerized deployment (Docker/Kubernetes) from an ownership and architecture standpoint, not just as a consumer.
• Familiarity with secure software development lifecycle (SDLC) practices — dependency management, secrets management, and security review — particularly relevant given the sensitivity of legal data.
• Experience architecting for reliability and scale: observability (logging, monitoring, alerting), incident response, and performance/cost tuning of production data services.
• Develop a critical understanding of business data to proactively identify opportunities to improve data quality and deliver solutions and insights. Partner with AI/ML and Product teams to build the data foundations (pipelines, feature stores, vector search, RAG-ready datasets) that power our GenAI and agentic product capabilities.
• Provide guidance for data integrity issues, ongoing maintenance, and data governance, including responsible-AI data practices such as lineage tracking and PII/PHI handling appropriate for legal data.
• Maintain detailed documentation of data collection methods and dependencies.
• Work with key stakeholders to understand BI/Analytics and reporting requirements.
• Conduct audits and troubleshoot tracking gaps to maintain data confidence and implement data standardization practices.
• Support a data-driven culture by educating the organization on everyday uses of data, and increasingly AI, as a resource.
• Work with our Data Science / Analytics organization to help achieve product goals, including supporting model evaluation and monitoring once in production.

Qualifications

• Minimum Bachelor’s degree in a related field of study required.
• Master’s degree in a related field is highly desirable.
• Minimum of 15-20 years of technology leadership experience.
• Minimum of 5 years of experience leading teams building data platforms.
• Understanding of machine learning, with prior experience implementing end-toend ML-powered models highly desirable; exposu…