• Six year of demonstrated experience in developing highly scalable, reliable, and real-time data processing pipelines combined with experience in Machine Learning workflow and model deployment
• Six years of demonstrated experience in designing and deploying software using frameworks for machine learning such as TensorFlow, Theano, Keras, Scikit-learn, Spark ML, CNTK, Matlab, Torch, Caffe, MXNet, H2O
• Five years of experience leading and software product development teams in an Agile environment with a variety of SQL and No-SQL data stores such as MongoDB, Cassandra, HBase, MySQL/Postgres
• Five years of demonstrated experience in developing data pipelines using Python/Java/Scala on various frameworks(especially on Apache Spark) on AWS, Azure, or similar cloud platforms; Demonstrated experience in Data security aspects and implementation.
• Candidate should aware of TFLite, ONNX, OpenVino, Sagemaker Neo, SNPE Model Optimization/Compilation tools.
• Candidate should have good knowledge in DL algorithms like, CNN, LSTM, RNN, Transformers, GAN.
• Should have Knowledge on Parallel Computing for Model training
• Should have knowledge on OpenCL/CuDNN/Cuda.
• Candidate should have good experience in DL/CV Algorithm Development.
• Candidate should basic knowledge on embedded platform.
• Candidate should have basic knowledge on Accelerators like FPGA, NPU, VPU, DSP, GPU, CP
• End-to-end pipeline building for ML/DL development.
• Should have basic knowledge on media frameworks like GStreamer, FFMPEG etc.

B.E/ B.Tech /M.Tech /M.C.A

• Lead a team of skilled engineers to build data pipelines and production level ML infrastructure in a fast-paced environment.
• Lead and manage your team of Data and ML engineers to translate Data & Analytics requirements in to short- and long-term implementation plans. Be comfortable with details and be hands-on to make sure the delivery expectations are met.
• Lead your team to launch new data ingestion, extraction, transformation and loading processes on AWS/Azure cloud with a keen focus on scalability, reliability, performance and reusability. Build key data sets and lead feature engineering efforts to empower exploratory analysis and advanced analytics.
• Collaborate with our data scientists to identify and build data pipelines and patterns that are relevant to advanced analytical model building and then curate, clean, wrangle and prepare data for efficient use at large scale.
• Lead your team to understand Machine Learning/Deep Learning model performance requirements, refactor model code as necessary, design model deployment frameworks and deploy models in prototype/production environments.
• Closely collaborate with other engineering teams to ship machine learning products to production.
• Interact with both business and technical stakeholders to deliver high quality products and services that meets/exceeds business customer, and technical requirements.
• Leverage your experience to evaluate new data technologies and build a scalable data engineering and ML engineering framework.
• Share in code and design reviews with agile team
• Integrate 3rd party software components into existing software applications
• Work with geographically distributed teams while maintaining highest standards in collaboration and communication.