Head of Engineering

CoreStory unlocks the hidden intelligence in your legacy code. By using AI to surface business logic and technical insights, we give enterprises the clarity to modernize faster, maintain apps smarter, and reduce the risk of costly failures.

We’re seeking a Head of Engineering to lead and scale the team responsible for CoreStory’s AI engine — the foundation that powers our intelligence platform. This is a hands-on leadership role for an engineering leader who combines deep technical understanding of AI systems with a passion for guiding a high-performing team.

The ideal candidate would be someone who has significant experience in, and understanding of, generative AI, both as a consumer and as a builder and wants to use their expertise to change the SDLC in the enterprise market.

As the Head of Engineering, you’ll lead CoreStory’s engineering team to design, build, and scale CoreStory’s agentic code intelligence platform.

You’ll define the AI engineering roadmap, ensure technical excellence, and drive execution across the team. Working cross-functionally with Product, Forward-Deployed Engineering, and Customer Success teams, you’ll translate business needs into robust, scalable, and intelligent AI capabilities that set CoreStory apart.

This role combines tactical and technical execution with strategic direction — ideal for someone who thrives at the intersection of architecture, leadership, and innovation.

• →Lead and grow the engineering team — hire, mentor, and develop engineers across product, platform, and infrastructure disciplines, building a culture of ownership, craft, and continuous improvement. Build a culture where engineers deeply understand and empathize with customers, not just internal requirements
• →Champion AI-native engineering practices.
• →Define and own the AI engineering roadmap, aligning technical direction with company and product strategy.
• →Oversee architecture and implementation of LLM-driven systems, including retrieval-augmented generation (RAG), chat agents, and semantic search pipelines.
• →Champion best practices for model evaluation, and AI feature integration.
• →Evaluate, integrate, and optimize LLMs (e.g., GPT, Claude, Llama, Mistral) for production use cases.
• →Collaborate with engineering leadership to ensure scalability, performance, and reliability across CoreStory’s Platform.
• →Foster a strong culture of experimentation, learning, and innovation across the engineering team.
• →Stay ahead of emerging trends in LLMs, RAG architectures, and AI frameworks — translating research into product-ready capabilities.
• →Own delivery velocity and execution — establish engineering processes, sprint cadences, and delivery practices that keep teams moving with clarity and confidence — without unnecessary overhead. Driving ownership of customer-facing engineering issues end-to-end

• 10+ years of experience in software or AI engineering, including 5+ years in a leadership role.
• Demonstrated experience working directly with customers (e.g., enterprise clients, design partners, or production users) to understand real-world use cases, pain points, and business impact
• A track record of hiring and developing strong engineers, and building team cultures where people do their best work. Proven ability to translate customer needs into engineering priorities, driving urgency, accountability, and high-quality outcomes
• Proven experience leading teams building agentic systems. Expertise in designing and building a from-scratch implementation of an agent network is highly desirable.
• Strong understanding of Agent Harnesses, RAG architectures, embedding pipelines, and tokenization.
• Demonstrated success leading end-to-end delivery of AI features in production environments.
• Excellent communication, collaboration, and leadership skills.

• Deep hands-on expertise with frameworks such as LlamaIndex, LangChain, or similar orchestration tools.
• Proficiency in Python, including libraries such as FastAPI, Claude SDK, DSPy.
• Experience leading AI initiatives in startup or SaaS environments.
• Familiarity with knowledge graphs, and hybrid IR systems.
• Background in LLM evaluation, AI observability, or model performance optimization.
• Passion for continuous learning and experimentation with cutting-edge AI research.