Senior Machine Learning Engineer with 5+ years delivering production systems in recommender ranking, multi-agent LLM/agentic workflows, and geospatial forecasting.
Hi, I’m Kumail Syed.
Senior Machine Learning Engineer with 5+ years delivering production systems in recommender ranking, multi-agent LLM/agentic workflows, and geospatial forecasting. Depth in problem framing, feature engineering, and end to end MLOps using Python, FastAPI, LangChain/LangGraph, Hugging Face, RAG with Pinecone/Qdrant, Docker, AWS/GCP, PostgreSQL/Redis. Results include a personalized ranking engine that lifted engagement 60%+, an LLM assistant that automated ~70% of support volume, and an ETA model that reduced operational delays by 15%. Agentic AI (goal-conditioned multi-agent workflows with planning, tool use, and memory), and geospatial forecasting. Partner cross functionally with product, data, and platform teams to turn messy data into measurable outcomes, using rigorous offline metrics, A/B testing, and online evaluation.
Senior Machine Learning Engineer with 5+ years delivering production systems in recommender ranking, multi-agent LLM/agentic workflows, and geospatial forecasting. Depth in problem framing, feature engineering, and end to end MLOps using Python, FastAPI, LangChain/LangGraph, Hugging Face, RAG with Pinecone/Qdrant, Docker, AWS/GCP, PostgreSQL/Redis. Results include a personalized ranking engine that lifted engagement 60%+, an LLM assistant that automated ~70% of support volume, and an ETA model that reduced operational delays by 15%. Agentic AI (goal-conditioned multi-agent workflows with planning, tool use, and memory), and geospatial forecasting. Partner cross functionally with product, data, and platform teams to turn messy data into measurable outcomes, using rigorous offline metrics, A/B testing, and online evaluation.
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.
