Lead AI Engineer

AI Solution Development and Delivery

• Design and implement scalable machine learning systems, including data pipelines, model training workflows, and real-time inference architectures.
• Develop and deploy generative AI applications, including Retrieval-Augmented Generation (RAG) pipelines, enterprise copilots, and intelligent assistants.
• Build and integrate AI solutions into production environments using APIs, microservices, and model-serving frameworks.
• Ensure robustness, scalability, and performance of AI systems in production.

 Applied Data Science and Machine Learning

• Develop predictive models, optimization algorithms, and deep learning systems for construction use cases.
• Apply statistical methods, feature engineering, and experimentation frameworks to evaluate and improve model performance.
• Conduct A/B testing and model validation to measure impact and reliability.

 Generative and Agentic AI Implementation

• Implement LLM-based applications including prompt engineering, embeddings, and vector search systems.
• Contribute to agent-based workflows and orchestration frameworks for semi-autonomous automation.
• Integrate structured and unstructured enterprise data into AI systems.

Technical Leadership

• Lead a team of ML engineers and data scientists to deliver high-quality AI solutions.
• Establish and enforce engineering standards, code quality practices, and system design principles.
• Guide debugging, optimization, and scaling of AI systems in distributed environments.

Collaboration and Execution

• Collaborate with product managers, business stakeholders, and engineering teams to align AI solutions with business objectives.
• Communicate technical concepts clearly to both technical and non-technical stakeholders.

Technical Skills and Expertise

• Strong programming expertise in Python
• Hands-on experience with deep learning frameworks such as PyTorch and TensorFlow
• Experience with Large Language Models (LLMs), RAG, embeddings, and vector databases
• Understanding of distributed systems and scalable AI architectures
• Experience with MLOps practices including CI/CD, monitoring, and model versioning
• Familiarity with AWS, Azure, or GCP

Qualifications

• 8–12 years of experience in AI/ML, data science, or software engineering
• Strong foundation in ML, deep learning, and statistics
• Experience delivering AI systems in production

Success Metrics

• Deployment of scalable AI solutions with measurable business impact
• Improvement in model performance and system reliability
• Adoption of AI solutions across teams

 Role in Construction AI Transformation

• Enable AI-driven decision-making across planning and execution
• Build AI copilots for engineering and project teams
  • Automate workflows and improve productivity