Senior AI/ML Engineer

Senior AI/ML Engineer-FULLTIME WITH CLIENT

Location: Richardson, TX||Hybrid

INPERSON INTERVIEWS ARE MANDATORY - NEED LOCALS

No visa sponsorship available

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 GCP).
• Familiarity with
  Docker, Kubernetes, and CI/CD pipelines
  .
• Strong communication skills and the ability to lead cross-functional initiatives.