Syed
Building end to end AI pipelines with Python, TensorFlow, PyTorch, and Scikit‑Learn containerized in Docker and orchestrated with Kubernetes.
Hi, I’m Kumail Syed.
with over five years of experience architecting and deploying scalable AI/ML solutions in healthcare, finance, and e-commerce domains. Expert in end to end MLOps (Docker, Kubernetes, Jenkins, CI/CD), real time streaming analytics (Kafka, spark), and NLP/vision pipelines (BERT, ResNet).
Senior Machine Learning Engineer with over five years of experience architecting and deploying scalable AI/ML solutions in healthcare, finance, and e-commerce domains. Expert in end to end MLOps (Docker, Kubernetes, Jenkins, CI/CD), real time streaming analytics (Kafka, spark), and NLP/vision pipelines (BERT, ResNet). Proven track record reducing model training time by 40%, cutting fraud-detection latency by 60%, and improving predictive maintenance accuracy by 25%.
Modern and mobile-ready website that will help you reach all of your marketing.
Music copying, writing, creating, transcription and composition services.
Advertising services include television, radio, print, mail, and web apps.
Developing memorable and unique mobile android, ios and video games.
A mid market client’s IT support center struggled with slow ticket resolution due to manual triage and routing. UIS Technology Partners implemented an AI driven ticket classification system to streamline support operations. The solution leveraged Natural Language Processing (NLP) to automatically categorize incoming IT support tickets by issue type, urgency, and department, allowing faster assignment to the right teams. This initiative modernized the client’s helpdesk with intelligent automation, improving response times and consistency in support.
TEKsystems partnered with a global talent solutions company (Allegis Group) to accelerate enterprise wide adoption of AI and Machine Learning by building a centralized MLOps and Data Science Hub. The project involved designing a hub and spoke architecture that could serve multiple business units, enabling data scientists to deploy and scale ML models more efficiently. Over an intensive 8 week engagement, the team delivered a production ready MLOps pipeline that standardized how AI/ML models move from prototype to deployment. This initiative exemplified leveraging cutting edge cloud AI services to transform a traditional organization into an AI enabled enterprise.
Apex Systems engaged with a health insurance provider to solve a data quality and efficiency problem using machine learning. The client’s operations involved matching lists of healthcare providers utilized by corporate policyholders against the insurer’s own provider network records. This process was previously done manually by a team and was extremely time consuming and error prone, due to inconsistencies in how provider names and addresses appeared across different data sources. Apex developed an AI powered provider matching solution that uses Natural Language Processing (NLP) and machine learning to automatically compare and reconcile provider information across multiple unstructured data sources. The result was a drastic reduction in manual effort (saving thousands of man hours) and improved accuracy in the network confirmation process.