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Summary:
The Head of Engineering (Vice President) for EverAI will play a pivotal role in leading the development of innovative AI-first contact center technology products and enterprise AI platforms. The role is focused on delivering cutting-edge solutions with speed, agility, scalability, and operational excellence. As the Head of Engineering, you will oversee all aspects of engineering, including AI platform engineering, full-stack development, infrastructure systems, databases, QA, and team management.
You will collaborate closely with Product Management, AI/ML teams, Solution Architects, Customer Success, and Sales teams to bring product visions to life and ensure our technology stack supports our strategic goals and customer commitments.
The role requires a highly hands-on engineering leader with strong exposure to modern GenAI/LLM ecosystems, enterprise-grade platform development, and customer-facing solutioning. The candidate should be comfortable operating in a hybrid environment where certain products/modules are developed in-house with proprietary IP, while other offerings leverage third-party platforms through strategic partnerships.
The individual will also actively contribute to technical pre-sales activities including customer demos, solution workshops, RFP/RFI responses, roadmap planning, annual operating plans and strategic technology initiatives for EverAI Labs.
Key Responsibilities:
Engineering Leadership:
Lead and manage a multidisciplinary engineering organization, including AI/ML engineers, full-stack developers, backend developers, platform engineers, DevOps, QA teams, architects, and engineering managers.
AI & Platform Engineering:
Drive architecture, development, and deployment of enterprise-grade AI applications, conversational AI systems, agentic workflows, knowledge assistants, analytics platforms, and automation products.
Technical Strategy:
Define and execute the technical strategy for scalable, secure, high-performance AI-native products and platforms aligned with business goals and product roadmap.
Hands-on Technical Oversight:
Provide deep technical guidance across:
- Python and Java ecosystems
- FastAPI and Spring Boot frameworks
- LLM orchestration frameworks such as LangChain, LlamaIndex, or Microsoft Semantic Kernel
- Retrieval-Augmented Generation (RAG) architectures
- Vector databases and semantic search systems
- Open-source and enterprise LLM ecosystems including Llama and Qwen models
- API-first and microservices-based architectures
- Cloud-native deployments and distributed systems
AI/LLM Solution Architecture:
Guide teams in designing:
- Context management and memory frameworks
- Evaluation and observability pipelines for LLM systems
- Secure enterprise AI deployments
- Model serving, inference optimization, and scalable AI runtime architectures
Product Development & Delivery:
Oversee end-to-end product engineering lifecycle, ensuring timely delivery of high-quality releases with strong engineering rigor, observability, resiliency, and maintainability.
Strategic Partnerships & Third-Party Platforms:
Work closely with strategic technology partners and evaluate third-party AI platforms/products for integration, customization, white-labeling, and enterprise deployment.
Team Building:
Recruit, mentor, and retain top engineering talent while fostering a culture of innovation, accountability, ownership, and continuous learning.
Collaboration:
Partner closely with Product Management, Design, AI Research, Infrastructure, Security, Customer Success, and Go-To-Market teams to align engineering execution with business priorities.
Customer & Pre-Sales Engagement:
Support Sales and Customer Success teams in:
- Technical demos and solution walkthroughs
- Customer workshops and architecture discussions
- RFP/RFI/RFQ responses
- Enterprise solution positioning
- Technical due diligence and client evaluations
- Discussions with customer infrastructure, security, and enterprise architecture teams
Process Improvement:
Implement and optimize engineering processes, development standards, CI/CD pipelines, AI governance practices, and quality frameworks across the organization.
Innovation:
Stay abreast of advancements in Generative AI, Agentic AI, LLMOps, MLOps, multimodal AI, speech technologies, and enterprise AI ecosystems to continuously evolve EverAI’s capabilities.
Roadmap & Strategic Planning:
Contribute to technology roadmap creation, engineering planning, platform strategy, annual operating planning (AOP), and long-term innovation initiatives for EverAI Labs.
Budget & Risk Management:
Manage engineering budgets, vendor relationships, infrastructure costs, and technical risks while ensuring optimal utilization of resources and sustainable platform scalability.
Stakeholder Communication:
Provide regular updates to leadership on engineering execution, risks, roadmap progress, scalability considerations, and strategic opportunities.
Qualifications:
Experience:
- 12+ years of experience in software engineering and platform development
- Minimum 5+ years leading engineering teams in high-growth product organizations
- Strong experience building enterprise SaaS platforms, AI-native products, or large-scale distributed systems
- Experience working with both proprietary product development and third-party platform integrations/white-label ecosystems
Technical Expertise:
Strong hands-on expertise in:
- Programming Languages: Python, Java
- Frameworks: FastAPI, Spring Boot
- LLM Frameworks: LangChain, LlamaIndex or Microsoft Semantic Kernel
- LLM Ecosystems: Llama, Qwen, and other open-source/commercial LLMs
- RAG architectures and vector databases
- API-driven and microservices architectures
- Cloud platforms and containerized deployments
- Enterprise system integrations
- CI/CD, DevOps, observability, and security best practices
Good-to-have exposure:
- STT/TTS systems and speech AI ecosystems
- Voice AI and conversational AI platforms
- LLMOps/MLOps tooling
- GPU inference optimization and model deployment
Leadership Skills:
Demonstrated ability to lead and scale high-performing engineering organizations with strong execution discipline in fast-paced environments.
Problem Solving:
Strong analytical and architectural problem-solving skills with the ability to navigate ambiguity and make pragmatic technical decisions.
Collaboration:
Excellent cross-functional collaboration skills with the ability to align engineering execution with product, business, and customer priorities.
Communication Skills:
Strong executive communication and customer-facing presentation skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
Education:
Bachelor’s degree in Computer Science, Engineering, or related field required. Master’s degree or equivalent experience preferred.
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If you’ve got the skills to succeed and the motivation to make it happen, we look forward to hearing from you.
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