Sr Advanced Software Engr - Bengaluru, India - Honeywell

Description

Honeywell is charging into the Industrial IoT revolution with the establishment of Honeywell Connected Enterprise (HCE), building on our heritage of invention and deep, on-the-ground industry expertise. HCE is the leading industrial disruptor, building and connecting software solutions to streamline and centralize the assets, people and processes that help our customers make smarter, more accurate business decisions. Moving at the speed of software, we are creating, innovating, and delivering solutions fast, challenging the way things have always been done, piloting new ways for all of us to work, and expecting our successes to set new standards for our customers and for Honeywell. Honeywell Forge ML platform will support the machine learning lifecycle from data ingestion to model training and monitoring, bring the customer ML algorithm/model (BYOML) , develop new algorithm , Inferencing , deploy model to any cloud, develop/serve LLM on Azure Databricks or any other data platform .

Summary:

· Lead the development and implementation of ML/AI platform using Azure tech stack and Databricks as the core components.

· Collaborate with cross-functional teams to analyze and extract insights around various use cases for the AI/ML platform.

· Utilize your expertise in cloud technologies for data preprocessing, feature engineering, and model training.

· Design and optimize scalable machine learning pipelines to deploy models in production environments.

· Stay updated on the latest advancements in machine learning, ensuring the integration of cutting-edge techniques into projects.

· Mentor and guide junior team members, fostering a collaborative and knowledge-sharing environment.

· Take ownership of end-to-end machine learning projects, from problem formulation to model deployment.

· Work closely with data engineers and data scientists to streamline data pipelines and enhance model performance.

· Contribute to the development of best practices and standards for machine learning workflows using tools like Databricks and MLflow.

· Collaborate with stakeholders to understand business requirements and translate them into actionable machine learning solutions.

· Ensure the reproducibility and version control of machine learning experiments.

· Experience in E2E system design and deployment of Data Pipeline and ML analytics.

Responsibilities:

· Extensive experience in constructing and maintaining a production-grade AI/ML platform that facilitates the deployment of ML models and algorithms. Proficient in monitoring data drift and ensuring seamless integration with diverse external systems.

· Demonstrate expertise in AI/ML concepts, encompassing Model Tracking, Model Serving, and other facets involved in operationalizing ML pipelines within distributed data processing environments such as Apache Spark. Utilize tools like MLflow to streamline these processess

· Bring professional proficiency in Big Data technologies, specifically Spark, Kafka, and the Databricks ecosystem. Proven programming skills in SQL and Python, Scala, or Java for production purposes

· Develop and maintain continuous integration build and deployment processes using platforms like Github, Bamboo, and Octopus.

Requirements:

Must-Have Skills:

· Demonstrate expertise in programming languages such as Python, Scala, and Java, essential for effective development and implementation of machine learning algorithms.

· Possess advanced skills in leveraging the Databricks platform, showcasing a deep understanding of big data processing and analytics for efficient model development and data manipulation.

· Knowledge and hands-on on orchestrator frameworks (Air-flow).

· Exhibit proficiency in working with cloud systems, particularly on the Azure platform, ensuring seamless integration of machine learning solutions with cloud-based infrastructure.

· Showcase expertise in MLOps frameworks and concepts, particularly with hands-on experience in MLflow. Demonstrate the ability to manage end-to-end machine learning lifecycle, from experimentation to deployment and monitoring.

· Display knowledge and experience in Continuous Integration and Continuous Deployment (CI/CD) processes using tools such as Github, Bamboo, and Octopus. Ensure the smooth integration of machine learning models into production environments.

Good-to-Have Skills:

· Possess a background in model monitoring, data drift detection, and other aspects of MLOps, ensuring the robustness and reliability of machine learning models over time.

· Knowledge on one of the Model building framework such as Tensorflow, KERA , Pytorch .

· Familiarity with data engineering solutions, including but not limited to Kafka, Event Hub, and Spark Streaming. Understand how these technologies can be integrated into machine learning workflows for efficient data processing.

· Have a good understanding of storage solutions like Druid and Delta Lake, showcasing the ability to handle and manage large volumes of data effectively. This knowledge contributes to creating resilient and scalable machine learning pipelines.

Additional Information