AI/ML Engineer

Ford Motor Company

We are looking for a highly skilled, technical, hands-on ML engineer with a solid background in building end-to-end AI/ML applications, exhibiting a strong aptitude for learning and keeping up with the latest advances in AI/ML. The candidate should also be proficient with AI literacy including Gen AI.

Responsibilities

The ML Engineer is expected to develop AI/ML Engineering Solutions, perform DevOps and work closely with other stakeholders (ML Engineers, Data Scientists, and Data Engineers) with key responsibilities to:

Develop ML Platform to empower Data Scientists to perform end to end ML Ops.
Work actively and collaborate with Data Science teams within Credit IT to design and develop end to end Machine Learning systems.
Lead evaluation of design options, tools, and utilities to build implementation patterns for MLOps using VertexAI in the most optimal ways.
Create solutions and perform hands-on PoCs.
Develop end to end and scalable Generative AI solutions.
Work with Suppliers, Google Professional Services, and other Consultants as required.
Collaborate with program managers to plan iterations, backlogs, and dependencies across all workstreams to progress the program at the required pace.
Collaborate with Data/ML Engineering architects, SMEs, and technical leads to establish best practices for data products needed for model training and monitoring considering regulatory policy and legal compliance.
Bachelor’s degree in computer science or related field.
8+ years of relevant work experience in solution, application, and ML engineering, DevOps with deep understanding of cloud hosting concepts and implementations.
Proven expertise with Vertex AI.
Very strong with programming in Python.
Knowledge of SQL (Relational & Non-relational).
5+ years of hands-on experience in Analytics, MLOps and Engineering Solutions for ML based models.
Knowledge of enterprise frameworks and technologies.
Strong in engineering design patterns, experience with secure interoperability standards and methods, engineering tools and processes.
Strong in containerization using Docker/Podman.
Strong understanding on DevOps principles and practices, including continuous integration and deployment (CI/CD), automated testing & deployment pipelines.
Good understanding of cloud security best practices and be familiar with different security tools and techniques like Identity and Access Management (IAM), Encryption, Network Security, etc.
Understanding of microservices architecture.
Strong leadership, communication, interpersonal, organizing, and problem-solving skills.
Strong in AI Engineering
The candidate needs to possess necessary Cloud experience (necessary) - preferably in GCP.
Demonstrated industry experience in developing end to end production grade AI/ML systems in both Traditional ML and Generative AI.
Proficiency in Agentic AI frameworks.

Preferred

Relevant certification in ML Engineering in GCP (GCP - Professional Machine Learning Engineer certification)