• Design, build, and maintain end-to-end MLOps pipelines using AWS SageMaker • Develop and manage Airflow DAGs for ML workflow orchestration (training, validation, deployment, retraining) • Automate model training, evaluation, versioning, and deployment • Implement CI/CD pipelines for ML workflows and model releases • Manage model lifecycle, including experimentation, deployment, monitoring, and retraining • Integrate data ingestion and feature engineering workflows with ML pipelines • Monitor model performance, data drift, and pipeline reliability • Collaborate closely with Data Scientists, Data Engineers, and DevOps teams • Ensure security, scalability, and cost optimization across ML infrastructure

• Work independently on client engagements, take part in the development of our practice, aid in business development, and contribute innovative ideas and initiatives to our company. • Become a trusted advisor working with clients to design end-to-end analytical solutions • Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights • Write code in SQL, Python, and Spark following software engineering best practices • Collaborate with stakeholders and customers to ensure successful project delivery

• Design, implement, and optimize FPGA logic using AMD/Xilinx toolchains (Vivado, Vitis, HLS) development in VHDL/Verilog • Integrate FPGA designs into larger systems, ensuring robust verification, documentation, and deployment across multiple platforms (Zynq, UltraScale+, Versal) • Develop innovative machine learning and computer vision solutions to analyze and exploit large, complex datasets from remote sensing phenomenology • Develop algorithms and associated software tools using C/C++/Python and associated machine learning libraries (PyTorch, LibTorch) • Train AI/ML models and tune their hyperparameters for a given dataset and algorithm objectives • Provide solutions for data collection and data linting that enable rapid, automated curation of training data • Keep up with the SoTA practices for AI/ML • Adhere to teams’ standards for reviewing source code, unit-testing, source code control, and documentation practices • Utilize Python PEP8 standards.

• Design, develop, and deploy robust ML systems and multi-model AI agents that solve real-world retail challenges. • Lead the entire lifecycle, including prototyping, deployment, monitoring, and maintenance using modern CI/CD and containerization practices. • Build high-performance data pipelines (ETL/ELT) for both training and real-time inference. • Act as a technical lead for the team, mentoring junior engineers and setting engineering best practices. • Partner with Product Managers and Data Scientists to translate business ambitions into sophisticated technical requirements.