I am a top-performing Data and Backend Engineer with extensive experience in high level design, RESTful APIs, performance optimization, parallelism, systems engineering. I am highly skilled in communicating with leadership teams to develop various needs and objectives and translate them into technical solutions and possess a perfectionistic mindset to put in extra work to make sure that the solution stands the test of time and changing requirements which the clients themselves will be unaware at present. I have a proven ability in evaluating existing solutions to identify inefficiencies and redundancies, innovating process improvements to optimize workflow, and drive achievement of short and long-term goals. I am recognized for fostering on-going professional development through team leadership, technical mentorship and academic guidance to team mates while dealing with technical challenges.
I am strong in Python and C# having also a strong background as a machine learning engineer and architecting highly scalable microservices and implementation. I was regarded as an innovative solution finder by peers; Has strong JAVA experience between 2015 and 2017.
Currently working as a freelancer with primary focus on backend and data. As an engineer with strong fundamentals and proven capability in adapting to unfamiliar technologies, I am also looking for opportunities in newer technologies which are not listed above in my profile.
Technologies: Python, GItlab CI/CD, Git, Flask, AWS, AWS IoT Core, Raspberry Pi 3, Wiregaurd VPN protocol, Networking
In my current role, I oversee all facets of technical solution design, development, and implementation. I guide team members to ensure adherence to performance metrics and overarching organizational goals. I leveraged LINQ and LIQ2Couchbase in daily operations.
Technologies: C#, REST, Git, Microservices, Solution Architecture, Rule Engines, .NET Core, Couchbase
I managed Python data engineering, using various approaches for data cleansing and imputation, including mean and median imputation and K-nearest neighbors. I developed impact analysis of various data inputs into the algorithm, determining usefulness in predicting market rate. I also researched patterns of parameters, including ‘time left for pickup" on final rate and implemented data pipeline based on results.
Technologies: Python, Git, Flask, Pandas, Numpy, Scikit-learn, XGBoost
Here, I facilitated back-end engineering for C# and Agile two-week releases, and unit testing.
Technologies: C#, REST, Git, Microservices, Unit testing, TDD, .NET Core, MS SQL Server, RASA NLU, Chatbots, Tensorflow
In this role, I automated tasks using JAVA, reading data from a web page, copying and parsing an Excel file in a remote service, connecting to DB and collating data, disseminating results in an email.