Company Description

The Bosch Group is a leading global supplier of technology and services. Since the beginning of 2013, its operations have been divided into four business sectors: Automotive Technology, Industrial Technology, Consumer Goods, and Energy and Building Technology.

The Bosch Group comprises Robert Bosch GmbH and its roughly 360 subsidiaries and regional companies in some 50 countries. If its sales and service partners are included, then Bosch is represented in roughly 150 countries. This worldwide development, manufacturing, and sales network is the foundation for further growth.

Bosch Global Software Technologies Company Limited (BGSV) is 100% owned subsidiary of Robert Bosch GmbH - one of the world’s leading global suppliers of technology and services, offering end-to-end Engineering, IT, and Business Solutions.

Starting its operation from 2010 at Etown 2 in HCMC, BGSV is the first software development center of Bosch in Southeast Asia. BGSV nowadays have over 4,000 associates, with a global footprint and presence in the US, Europe, and the Asia Pacific region.

With our unique ability to offer end-to-end solutions that connect sensors, software, and services, we enable businesses to move from the traditional to digital or improve businesses by introducing a digital element in their products and processes.

Job Description

This position will be contracted through Bosch’s external vendor under a one-year agreement.

The Senior AI Engineer for this global workflow product plays a specialized role at the intersection of reverse engineering and forward-looking product development. Unlike standard AI roles, this position focuses heavily on "unlocking" decades of tribal knowledge and operational data hidden in legacy silos to power the next generation of AI-driven automation.

You will lead the technical strategy for extracting, mining, and transforming massive volumes of legacy data into high-quality training sets for AI models. Your mission is to evolve our global workflow product from a reactive IT tool into a proactive, AI-driven platform capable of predictive maintenance, anomaly detection, and automated decision-making.

Key Responsibilities

• Legacy Data Mining: Architect and implement advanced data mining techniques to extract valuable operational insights from structured and unstructured legacy systems (e.g., old SQL procedures, mainframe logs, siloed documentation).
• AI Product Evolution: Design and deploy production-ready ML/Deep Learning models that drive core product features such as intelligent workflow routing, natural language interfaces (LLMs), and predictive analytics.
• Feature Engineering for Workflows: Lead the discovery and creation of "features" from legacy data that specifically improve IT workflow efficiency, such as identifying bottleneck patterns or predicting system failures.
• Global Model Management: Oversee the deployment and lifecycle (MLOps) of models across global regions, ensuring low-latency performance and compliance with regional data residency laws (e.g., GDPR, SOC2).
• Reverse Engineering via GenAI: Utilize Generative AI and LLMs to automate the modernization of legacy codebases, turning outdated scripts into documented, cloud-native services.
• Cross-Functional Leadership: Mentor junior engineers and collaborate with Product Managers to translate legacy business logic into modern, AI-powered user experiences.

Qualifications

Technical Skills & Experience

• Education: Bachelor's or Master’s degree in Computer Science, Artificial Intelligence, or a related quantitative field.
• Experience: 8+ years in software or data engineering, with at least 4+ years specialized in AI/ML deployment in a global production environment.
• AI/ML Mastery: Expert proficiency in Python and frameworks like PyTorch, TensorFlow, or Scikit-learn. Strong experience with LLM Orchestration (e.g., LangChain, LangGraph).
• Data Mining & Engineering: Deep experience with ETL/ELT pipelines, NoSQL/Graph databases, and tools like Apache Spark or Flink for large-scale data processing.
• Infrastructure: Hands-on experience with MLOps (MLflow, Airflow), containerization (Docker, Kubernetes), and global cloud platforms (AWS, Azure, or GCP).

Specialized Hybrid Skills

• Legacy System Knowledge: Ability to navigate and extract data from older relational databases or siloed enterprise architectures.
• Analytical Mindset: Proven track record of identifying high-value AI use cases from messy, real-world "dark data".
• Strategic Communication: Ability to justify the ROI of legacy data mining projects to senior leadership.

Additional Information

Further details regarding benefits will be shared during the interview process