Lead/Senior Engineer - Python

<p><p><b>About the Opportunity : <br/></b><br/>We are seeking a highly skilled Lead / Senior Engineer Python / Data / AI to design and build large-scale data-driven systems and AI-powered solutions.<br/><br/>This is a hands-on technical leadership role that combines the depth of Python development, data engineering, and machine learning expertise with the architectural understanding of cloud-native AI platforms.<br/><br/>The ideal candidate will bring experience in developing scalable data pipelines, deploying ML models, and applying modern AI frameworks (TensorFlow, PyTorch) to solve business problems.<br/><br/>This position offers the chance to contribute to full-stack AI solution design from data ingestion to model training and deployment in a dynamic, innovation-focused environment.<br/><br/><b>What Youll Do : </b><br/><br/>- Design, develop, and maintain data engineering pipelines using Python, PySpark, and SQL for structured and unstructured data.<br/><br/>- Build, train, and optimize machine learning and deep learning models using TensorFlow and PyTorch.<br/><br/>- Implement scalable data solutions across cloud environments such as Azure, AWS, or GCP.<br/><br/>- Collaborate with data scientists and product teams to productionize ML models and ensure smooth deployment through MLOps pipelines.<br/><br/>- Architect and implement ETL/ELT processes that ensure data quality, security, and compliance.<br/><br/>- Perform feature engineering, data validation, and performance tuning to enhance model efficiency and accuracy.<br/><br/>- Leverage distributed computing frameworks (Spark, Databricks, or similar) for large-scale data processing.<br/><br/>- Collaborate closely with software engineers, data analysts, and business stakeholders to translate analytical insights into scalable applications.<br/><br/>- Define best practices for data pipelines, version control, and CI/CD automation within the AI/ML lifecycle.<br/><br/>- Stay current with emerging technologies in AI, data engineering, and MLOps, and recommend their adoption when appropriate.<br/><br/><b>What You Bring : </b><br/><br/>- 5 to 12 years of hands-on experience in Python development, data engineering, and machine learning.<br/><br/>- Strong proficiency in Python, SQL, and PySpark, with experience in building end-to-end data workflows.<br/><br/>- Expertise in one or more AI/ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.<br/><br/>- Proven experience with data modeling, ETL/ELT design, and data pipeline orchestration in production environments.<br/><br/>- Solid understanding of cloud computing platforms Azure, AWS, or GCP including data and AI services.<br/><br/>- Knowledge of MLOps practices, model deployment strategies, and CI/CD pipelines for ML workflows.<br/><br/>- Familiarity with distributed computing and big data frameworks (Spark, Databricks, or Hadoop).<br/><br/>- Strong problem-solving and debugging skills with a focus on data quality, scalability, and performance optimization.<br/><br/>- Excellent communication and leadership skills to mentor junior engineers and collaborate across technical and business teams.<br/><br/>- Bachelors or Masters degree in Computer Science, Data Science, or a related engineering discipline.<br/><br/><b>Preferred Skills : </b><br/><br/>- Exposure to Generative AI (LLMs, embeddings, prompt engineering) or NLP pipelines.<br/><br/>- Familiarity with containerization (Docker, Kubernetes) and serverless ML deployments.<br/><br/>- Experience in data versioning and model monitoring tools such as MLflow, DVC, or Kubeflow.<br/><br/>- Understanding of streaming data systems (Kafka, Kinesis) and real-time analytics architectures.<br/><br/>- Prior experience working with cross-functional data science teams on AI-driven products</p><br/></p> (ref:hirist.tech)