Sr AI​/ML Lead Engineer

Overview

Role:
Sr AI/ML Engineer
Client: CBRE
Location:
Richardson, TX

Mode: FTE

We are seeking a hands-on Lead AI/ML Engineer to design, build, and operationalize intelligent, scalable AI systems that solve complex business problems. This role goes beyond experimentation; you will deeply understand existing enterprise systems
, integrate Agentic AI and Generative AI solutions
, and drive production-grade AI platforms from concept to deployment.

Expecting strong engineering execution with applied AI/ML, enabling automation, conversational intelligence, and data-driven decision-making across the organization.

• Design, develop, and deploy production-grade machine learning models for prediction, classification, anomaly detection, and optimization.
• Work hands-on with Python-based AI/ML stacks to build scalable and maintainable solutions.
• Continuously evaluate model performance and apply improvements to accuracy, robustness, and efficiency.
• Build and operationalize Generative AI solutions using LLMs, embeddings, and retrieval techniques.
• Design Agentic AI workflows that automate multi-step reasoning, decision-making, and task execution.
• Implement RAG pipelines
  , vector databases, prompt orchestration, and memory frameworks.
• Integrate GenAI systems seamlessly into existing enterprise applications and data platforms
  .
• Architect and enhance conversational AI systems
  , including chatbots and virtual assistants.
• Develop multi-turn dialogue systems leveraging LLMs, transformers, and contextual memory
  .
• Exposure to Ontology and Knowledge Graphs is a strong plus for semantic understanding and contextual retrieval.

• Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field
  .
• Proven hands-on experience in AI/ML engineering with strong proficiency in Python
  .
• Proven experience delivering production AI/ML solutions (not just research or PoCs).
• Strong expertise in ML frameworks and libraries (Tensor Flow, PyTorch, scikit-learn, etc.).
• Hands-on experience with LLMs, NLP, and Generative AI frameworks
  .
• Solid understanding of neural networks
  , including CNNs, RNNs, transformers, and attention mechanisms.
• Experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
• Familiarity with Docker, Kubernetes, and CI/CD pipelines
  .
• Strong communication skills and the ability to lead cross-functional initiatives.