Lead AI Engineer

Job Title: Lead AI Engineer
Location: Atlanta, GA (EST time zone preferred)
Employment Type: Contract
REMOTE

GC/USC

7+ years in AI/ML and data engineering, expertise in Agentic AI frameworks (Lang Graph, CrewAI, Semantic Kernel), Python, generative AI, RAG/NLP, Vector DBs, Knowledge Graphs, GCP (Vertex AI, Big Query, Cloud Functions, Pub/Sub), and ML frameworks (Tensor Flow, PyTorch, Scikit-learn).

Role Summary:
We are seeking a Lead AI Engineer to drive the design and development of a next-generation Agentic AI platform. This high-impact role will shape the technical strategy and infrastructure behind autonomous systems, including LLM-based pipelines, agent orchestration, and vector-based memory. The ideal candidate combines strategic vision with hands-on expertise, delivering scalable, intelligent AI-driven solutions.

Key Responsibilities:

• Lead the design, architecture, and development of scalable Agentic AI systems.
• Build and optimize LLM pipelines, agent orchestration frameworks, and memory systems using vector databases.
• Implement core infrastructure on GCP, leveraging services such as Vertex AI, Big Query, Cloud Functions, and Pub/Sub.
• Develop, fine-tune, and evaluate ML models and orchestration workflows.
• Drive technical strategy, tooling, and infrastructure decisions across AI initiatives.
• Conduct model evaluation, A/B testing, and performance benchmarking.
• Collaborate effectively with cross-functional Agile teams to deliver high-impact solutions.
• Stay up-to-date with emerging research and technologies in agentic and generative AI.

Key Requirements:

• 7+ years of experience in AI/ML, data engineering, and large-scale program delivery.
• Deep expertise in Agentic AI architectures and frameworks (e.g., Lang Graph, CrewAI, Semantic Kernel).
• Strong proficiency in Python for scalable application development and performance optimization.
• Hands-on experience with generative AI, NLP, and RAG (Retrieval-Augmented Generation) architectures.
• Proven experience designing and implementing IR/RAG systems with Vector DBs and Knowledge Graphs.
• Familiarity with cloud-native development and GCP services.
• Experience with ML frameworks such as Tensor Flow, PyTorch, and Scikit-learn.
• Excellent communication skills and ability to adapt in fast-paced, innovative environments.