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AI/ML Lead Engineer (Richardson)GBIT (Global Bridge InfoTech Inc) • Richardson, TX, US
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AI / ML Lead Engineer (Richardson)
GBIT (Global Bridge InfoTech Inc) • Richardson, TX, US
[job_card.variable_days_ago]
[job_preview.job_type]
- [job_card.full_time]
- [job_card.part_time]
[job_card.job_description]To perform this job optimally, an individual will need to perform each crucial duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and / or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions. Bachelors or Masters degree in Computer Science, Data Science, Engineering, or related field. 5+ years of experience in AI / ML engineering, with a solid base in Python Proficient in ML libraries & toolkits, predictive modeling, pattern recognition, analytics, etc. Proven experience with LLMs, NLP, and generative AI frameworks Understanding of neural network architectures including CNNs, RNNs, transformers, and attention mechanisms. Expertise in knowledge graph construction and integration with conversational systems. Familiarity with MLOps, model lifecycle management, and secure data governance practices. Experience with cloud platforms (Azure, AWS, GCP), containerization (Docker / Kubernetes), and CI / CD pipelines. Strong communication and leadership skills to drive multi-functional initiatives and present to executive audiences.
Role : AI / ML Lead Engineer
Fulltime role
Location : Richardson, TX||Hybrid
Job Details
Were seeking a AI / ML Lead Engineer to lead the design and implementation of scalable, intelligent systems that solve sophisticated business problems.
This role is pivotal in transforming data into actionable insights using advanced machine learning, generative AI, and conversational technologies.
Responsibilities :
- Business Analytics & Intelligence : Analyze large datasets to uncover trends, patterns, and insights using statistical and machine learning techniques.
- Model Engineering : Design, train, and deploy ML models, classifiers, and algorithms for predictive analytics, anomaly detection, and optimization.
- Generative & Agentic AI : Build and operationalize generative AI and agentic frameworks using RAG pipelines, vector databases, and prompt chaining.
- Conversational AI Development : Architect and fine-tune intelligent virtual assistants and multi-turn dialogue systems using LLMs, transformers, and knowledge graphs.
- Knowledge Graph Integration : Leverage semantic modeling and graph databases to enhance contextual understanding and retrieval in AI systems.
- Enterprise Fine-Tuning : Apply domain-specific fine-tuning techniques (DPO, ORPO, SPIN) to align LLMs with enterprise knowledge and workflows.
- AI Infrastructure : Develop robust ML pipelines using AI / ML Ops tools
- Risk Mitigation : Identify and address risks in AI / ML systems including bias, drift, and adversarial vulnerabilities. Implement safeguards and monitoring strategies.
- Multi-functional Collaboration : Work with data scientists, engineers, and business collaborators to align AI solutions with strategic goals.
- Collaborator Involvement : Present captivating demonstrations and recommendations to business collaborators, translating technical insights into strategic suggestions.
- Research & Innovation : Stay ahead of the curve by exploring emerging trends in AI safety, interpretability, and bias mitigation.
Expectations :
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