Sr AI/ML Lead Engineer

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.

Qualifications

• 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 (TensorFlow, 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.