Machine Learning Engineer

Global Data Insight & Analytics organization is looking for a top-notch Software Engineer who has also got Machine Learning knowledge & Experience to add to our team to drive the next generation of AI/ML (Mach1ML) platform. In this role you will work in a small, cross-functional team. The position will collaborate directly and continuously with other engineers, business partners, product managers and designers from distributed locations, and will release early and often. The team you will be working on is focused on building Mach1ML platform - an AI/ML enablement platform to democratize Machine Learning across Ford enterprise (like OpenAI s GPT, Facebook s FBLearner, etc.) to deliver next-gen analytics innovation.

We strongly believe that data has the power to help create great products and experiences which delight our customers. We believe that actionable and persistent insights, based on high quality data platform, help business and engineering make more impactful decisions.

Our ambitions reach well beyond existing solutions, and we are in search of innovative individuals to join this Agile team. This is an exciting, fast-paced role which requires outstanding technical and organization skills combined with critical thinking, problem-solving and agile management tools to support team success.

• Bachelor s or master s degree in computer science engineering or related field or a combination of education and equivalent experience.
• 3+ years of experience in full stack software development
• 3+ years experience in Cloud technologies & services, preferably GCP
• 3+ years of experience of practicing statistical methods and their accurate application e.g. ANOVA, principal component analysis, correspondence analysis, k-means clustering, factor analysis, multi-variate analysis, Neural Networks, causal inference, Gaussian regression, etc.
• 3+ years experience with Python, SQL, BQ.
• Experience in SonarQube, CICD, Tekton, terraform, GCS, GCP Looker, Google cloud build, cloud run, Vertex AI, Airflow, TensorFlow, etc.,
• Experience in Train, Build and Deploy ML, DL Models
• Experience in HuggingFace, Chainlit, React
• Ability to understand technical, functional, non-functional, security aspects of business requirements and delivering them end-to-end.
• Ability to adapt quickly with opensource products & tools to integrate with ML Platforms
• Building and deploying Models (Scikit learn, DataRobots, TensorFlow PyTorch, etc.)
• Developing and deploying On-Prem & Cloud environments
• Kubernetes, Tekton, OpenShift, Terraform, Vertex AI

• Work closely with Tech Anchor, Product Manager and Product Owner to deliver machine learning use cases using Ford Agile Framework.
• Work with Data Scientists and ML engineers to tackle challenging AI problems.
• Work specifically on the Deploy team to drive model deployment and AI/ML adoption with other internal and external systems.
• Help innovate by researching state-of-the-art deployment tools and share knowledge with the team.
• Lead by example in use of Paired Programming for cross training/upskilling, problem solving, and speed to delivery.
• Leverage latest GCP, CICD, ML technologies
• Critical Thinking: Able to influence the strategic direction of the company by finding opportunities in large, rich data sets and crafting and implementing data driven strategies that fuel growth including cost savings, revenue, and profit.
• Modelling: Assessments, and evaluating impacts of missing/unusable data, design and select features, develop, and implement statistical/predictive models using advanced algorithms on diverse sources of data and testing and validation of models, such as forecasting, natural language processing, pattern recognition, machine vision, supervised and unsupervised classification, decision trees, neural networks, etc.
• Analytics: Leverage rigorous analytical and statistical techniques to identify trends and relationships between different components of data, draw appropriate conclusions and translate analytical findings and recommendations into business strategies or engineering decisions - with statistical confidence
• Data Engineering: Experience with crafting ETL processes to source and link data in preparation for Model/Algorithm development. This includes domain expertise of data sets in the environment, third-party data evaluations, data quality
• Visualization: Build visualizations to connect disparate data, find patterns and tell engaging stories. This includes both scientific visualization as well as geographic using applications such as Seaborn, Qlik Sense/PowerBI/Tableau/Looker Studio, etc.