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Director Software Engineering

Epsilon
Karnataka, IND
Full-time
Onsite
Discovered 3 weeks ago
AI/ML technologies and frameworksLarge Language Models (LLMs)Retrieval-Augmented Generation (RAG)Agentic design patternsVector databases and semantic searchLangChain and LlamaIndex

Full Job Posting

Role overview

Lead transformation of digital marketing capabilities through AI solutions and provide technology thought leadership.

Serve as Technology Chapter Leader, developing technical talent, engineering excellence, and future capabilities.

Responsibilities

  • Set the strategy for integrating AI into technology offerings and architect AI-powered solutions for client and internal needs.
  • Partner with leadership on technical strategies and solution architectures; guide architecture, design, and technical decisions.
  • Create frameworks, procedures, and standards for AI-driven solutions; mentor technical leaders and teams.
  • Shape platform roadmaps with product and engineering teams and drive proofs of concept, pilots, and research.
  • Lead hiring, capability development, career growth, engineering standards, and succession planning for the Technology Chapter.

Qualifications

  • 14+ years in technology leadership roles and a record leading enterprise solution architecture and engineering teams.
  • Practical knowledge of AI/ML and LLM solution design; expertise in RAG and understanding of agentic patterns, vector databases, and semantic search.
  • Knowledge of responsible AI and governance; familiarity with LangChain or LlamaIndex is requested.
  • Cloud architecture across AWS, Azure, and GCP; API design, integration patterns, AI-ready data architecture, microservices, and containerization.
  • Experience driving engineering modernization and AI-assisted development.
  • Preferred: digital transformation leadership, mentoring technical talent, or building AI-enabled engineering organizations.
  • Preferred: advanced degree or equivalent experience in computer science, engineering, AI, or a related field.

Success measures

Increase adoption of AI-powered solutions and reusable AI assets and architectural patterns.

Reduce solution delivery time and implementation complexity.

Develop technology leaders and engineering talent, and expand organizational AI capabilities.

Improve engineering quality, productivity, and innovation.

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