Director of Engineering Artificial Intelligence Foundry at Insight Global, Atlanta, GA
Director of Engineering Artificial Intelligence Foundry at Insight Global in Atlanta, GA. Est. up to $250k. Apply with swipeandhired.
[Evergreen]
Overview
As Director of Engineering, you will lead the design, development, and engineering of enterprise-grade agentic AI solutions and frameworks for Evergreen.AI. This role requires a proven leader who can scale engineering teams, define technical strategy, and ensure operational excellence for production systems-not just PoCs and pilots. You will leverage your experience in technical architecture, global delivery leadership, and AI enablement to build secure, resilient, and compliant solutions for Fortune 500 clients.
In addition, you will serve as a highly client-facing leader, engaging directly with executive stakeholders to understand business needs, communicate technical concepts clearly, and build trusted advisory relationships. You will foster a collaborative, solution-oriented culture, demonstrating strong communication skills and a growth mindset to drive innovation and continuous improvement across teams.
Responsibilities
- Engineering Leadership: Build and lead high-performing engineering teams across regions; establish career frameworks, mentorship programs, and succession planning.
- Platform & Framework Ownership: Define Evergreen.AI’s agentic AI architecture, including multi-agent orchestration, LLM knowledge management, and enterprise integration patterns.
- Delivery Excellence: Drive production readiness-runbooks, observability, SLAs, and resiliency patterns for multi-region deployments.
- Technical Strategy: Partner with Product and Architecture to align roadmaps with business outcomes; evaluate emerging technologies for scalability and compliance.
- Operational Governance: Implement secure SDLC, CI/CD, LLMOps/MLOps, and DevSecOps practices; ensure adherence to SOC 2, ISO 27001, HIPAA, and GDPR standards.
- Own end-to-end ML lifecycle including data ingestion, preprocessing, model training, serving, and evaluation; ensure reproducibility, traceability, and versioning of models and experiments; implement production-grade MLOps practices (CI/CD for ML, automated validation, monitoring, rollback strategies).
- Client Engagement: Support executive briefings, architecture reviews, and technical pre-sales; act as a trusted advisor for enterprise AI adoption.
- Innovation & Enablement: Champion responsible AI principles; contribute to reusable accelerators, reference architectures, and delivery templates.
- Team Collaboration & Communication: Foster a culture of teamwork and open communication, supporting and empowering colleagues across engineering, data science, product, and business functions. Build consensus, resolve conflicts constructively, and celebrate team achievements.
- Solution Orientation: Approach challenges with creativity and resilience, focusing on outcomes and continuous improvement. Proactively identify obstacles, develop actionable plans, and drive execution to deliver measurable business value for clients and the organization.
- Growth Mindset: Embrace learning, innovation, and personal development. Stay current with emerging technologies, encourage experimentation, and foster an environment where feedback is welcomed and used for improvement.
Qualifications
- 12+ years in software engineering, with 5+ years leading multi-team engineering organizations delivering enterprise-grade AI solutions.
- Proven experience in technical architecture and global delivery leadership for Fortune 1000 clients.
- Expertise in agentic AI/ML systems, orchestration frameworks (LangChain, Semantic Kernel), and LLMOps/MLOps platforms (MLflow, Kubeflow, Azure ML).
- Strong knowledge of data and knowledge management for LLMs, including retrieval pipelines and vector databases (Pinecone, Weaviate, Milvus).
- Hands-on experience with cloud platforms (Azure preferred), container orchestration (Kubernetes), and event-driven architectures (Kafka/Event Hub).
- Familiarity with observability tools (Prometheus, Grafana, ELK) and resiliency patterns (circuit breakers, chaos engineering).
- Strong proficiency with Python and ML frameworks
- Exceptional leadership, cross-collaboration, communication, and stakeholder management skills.
- Advanced degree in Computer Science.