Lead AI Engineer

vConstruct seeks a Lead AI Engineer to design, build and deploy production-grade AI solutions for construction workflows. The role combines hands-on machine learning, data science and generative AI development with technical leadership. The listed locations are Pune and Nagpur.

Responsibilities

• Design scalable ML systems, including data pipelines, model-training workflows and real-time inference architectures. Deploy and integrate solutions through APIs, microservices and model-serving frameworks, ensuring production performance, robustness and scalability.
• Build generative AI applications such as retrieval-augmented generation pipelines, enterprise copilots and intelligent assistants. Implement LLM applications using prompts, embeddings and vector search; contribute to agent-based workflows and orchestration, and integrate structured and unstructured enterprise data.
• Develop predictive models, optimization algorithms and deep learning systems for construction use cases. Use statistical methods, feature engineering, experimentation, A/B testing and model validation to improve performance and assess impact.
• Lead ML engineers and data scientists; establish engineering standards and code-quality practices; guide system design, debugging, optimization and scaling in distributed environments. Work with product managers, business stakeholders, data engineers and technical leads to align solutions and data models with business needs.

Qualifications and expertise:

• 8–12 years of experience in AI/ML, data science or software engineering; a strong foundation in machine learning, deep learning and statistics; and experience delivering AI systems to production.
• Strong Python programming; hands-on PyTorch and TensorFlow experience; experience with LLMs, RAG, embeddings and vector databases; understanding of distributed systems and scalable AI architecture; MLOps practices including CI/CD, monitoring and model versioning; and familiarity with AWS, Azure or GCP.

The work aims to enable decision-making across construction planning and execution, provide copilots for engineering and project teams, and automate workflows. Success is measured through business impact, model performance, system reliability and adoption across teams.