דרושים Mgr-ml Enablement Engineering ב-Ford ב-India, Chennai

The IT Senior Engineering Manager leads products to build experiences for Owners working with interactions and integrations across the enterprise. This is a global responsibility, providing the IT point of view on the proposed business requirements to integrate into the existing product portfolios. You will need to understand architecture, technology, and implementation strategies to optimize the experience and the efficiencies across the enterprise. In addition, you will be hands on in this leadership role, working with the team and Product Owner and stakeholders – this is not just a role of pure delegation. You will analyze impact of change on KPI’s and interactions with the customer to further provide Business Value.

• Transform teams from a project mindset towards a product mindset
• Engineering leadership role (LL6) overseeing design and delivery of a complex ‘ecommerce search experience’ micro services ecosystem
• This team is responsible for the systems and technology powering the product discovery and other core product related experiences on ford.com and the other regional markets, as part of the wholistic Omni-Channel, global, platform Commerce team
• Expected to possess experience or at least exposure in GenAI platforms – Multi Modal RAG, Semantic search, Vector DBs, Integrate LLM models to power business use cases and lead all AI related features on ford.com
• In partnership with the principal architect, who is responsible for overall architecture & design, build and deliver a modern, greenfield open stack platform based on MACH architecture principles
• Model the use of modern software engineering practices (TDD BDD, CI/CD, Shift left, 12 factor applications, API first design language etc..) to support integration with existing Ford software products as well as external cloud-based services.
• Collaborate with Ford’s Enterprise Architecture organization to rationalize technologies, further leverage current technology offerings already in use, and identify gaps/opportunities.
• Implement a talent system to review existing team capabilities (internal and external) and build and promote practices to develop a world-class software engineering team.
• Provide thought leadership for the development, structure, technology of Ford with skills in strategic planning, goal setting, processes development, budgeting, and creating opportunities for professional development within technology teams
• Strategize & work with senior leaders across Ford’s engineering organization to make significant, measurable impact on a global scale by innovating and operating with an iterative, agile, and user-centric perspective

We are seeking a highly skilled and experienced Product Team Technical Supervisor to lead one of our key product teams within the Industrial System Analytics (ISA) product group, focusing on Design Cost & Complexity. This role reports directly to the Product Line Owner and is critical for driving the development and delivery of cloud-native analytical solutions that integrate robust software engineering with advanced data science capabilities.

• Strong programming skills, particularly in Python, with experience in ML frameworks like TensorFlow or PyTorch.
• Proven expertise in MLOps best practices including model versioning, monitoring, automated deployment, and infrastructure automation.
• In-depth knowledge of Google Cloud Platform services including Data Fusion, Dataproc, Dataflow for data processing and pipeline orchestration.
• Experience working with PostgreSQL databases and data integration tools like Qlik Replicate.
• Expertise in security and privacy considerations for machine learning systems, including data encryption, access control, and compliance with regulations such as GDPR and HIPAA.
• Strong communication and leadership skills, capable of engaging both technical and non-technical stakeholders effectively.

This specialist combines expertise in cybersecurity and AI/ML to design, implement, and maintain security frameworks, ensuring the integrity, confidentiality, and compliance of AI-driven solutions throughout their lifecycle. This also involves collaboration with cross-functional, stakeholders and AI Engineers to build and deploy enterprise-wide AI security framework.