- Company Name
- GBIT (Global Bridge InfoTech Inc)
- Job Title
- AI/ML Lead Engineer
- Job Description
-
**Job Title**
AI/ML Lead Engineer
**Role Summary**
Lead the design, development, and deployment of scalable intelligent systems that convert complex data into actionable insights. Drive the integration of machine learning, generative AI, conversational AI, and knowledge graph technologies to solve business challenges and align solutions with strategic objectives.
**Expectations**
- 5+ years in AI/ML engineering with a strong foundation in Python.
- Proven expertise in LLMs, NLP, and generative AI frameworks.
- Deep understanding of neural network architectures (CNNs, RNNs, transformers, attention).
- Experience with MLOps, model lifecycle, and secure data governance.
- Proficiency with cloud platforms (Azure, AWS, GCP), containerization (Docker/Kubernetes), and CI/CD pipelines.
- Ability to collaborate cross‑functionally, communicate complex concepts to executive stakeholders, and lead initiatives.
**Key Responsibilities**
- Analyze large datasets to identify trends, patterns, and actionable insights using statistical and machine learning techniques.
- Design, train, validate, and deploy predictive analytics models, classifiers, and optimization algorithms.
- Build and operationalize generative and agentic AI solutions using RAG pipelines, vector databases, and prompt chaining.
- Architect, fine‑tune, and maintain conversational AI systems (LLMs, transformers, multi‑turn dialogue) with knowledge graph integration.
- Apply domain‑specific fine‑tuning (DPO, ORPO, SPIN) to align LLMs with enterprise content and workflows.
- Develop robust ML pipelines and MLOps practices to ensure model reproducibility, monitoring, and governance.
- Identify and mitigate risks related to bias, drift, and adversarial vulnerabilities, implementing safeguards and monitoring.
- Collaborate with data scientists, engineers, and business partners to align AI solutions with organizational goals.
- Deliver demonstrations and strategic recommendations to stakeholders, translating technical findings into business value.
- Monitor industry trends in AI safety, interpretability, and bias mitigation; drive innovation within the team.
**Required Skills**
- Python programming and ML libraries (scikit‑learn, PyTorch/TensorFlow, Hugging Face).
- Machine learning fundamentals: predictive modeling, pattern recognition, anomaly detection.
- Generative AI knowledge: RAG, large language models, prompt engineering.
- Conversational AI: architecture, fine‑tuning, dialog management.
- Knowledge graph construction and integration (graph databases, semantic modeling).
- MLOps practices: model deployment, lifecycle management, monitoring.
- Cloud computing (Azure, AWS, GCP), containerization (Docker, Kubernetes), CI/CD pipelines.
- Strong analytical, problem‑solving, and communication skills.
**Required Education & Certifications**
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.
- Relevant certifications in AI/ML, cloud platforms, or data engineering are an advantage.
Richardson, United states
Hybrid
Senior
25-11-2025