AI/ML Developer (Data Scientist)

United States Digital Space LLC; Karnataka; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Autoliv in India is seeking an AI/ML Developer (Data Scientist) to design, develop, and deploy scalable AI/ML solutions across manufacturing and operations. You will transform data into actionable insights, and work with business leaders to drive measurable value.

The role requires 6+ years in DS/ML, strong Python, ML libs, SQL, and experience with DL/NLP/CV. You will deploy production models, build MLOps pipelines, and collaborate with IT and cloud teams to ensure secure, robust deployments.

Qualifications

• 6+ years of professional experience in Data Science, ML, AI, or Advanced Analytics.
• Strong knowledge of ML algorithms including regression, classification, clustering, anomaly detection, and time-series forecasting.
• Experience with Deep Learning, NLP, Computer Vision, or Generative AI technologies.
• Advanced programming skills in Python with libraries such as NumPy, Pandas, Scikit‑learn, TensorFlow, and PyTorch.
• Strong SQL skills and experience with large-scale datasets.
• Experience using data visualization tools such as Power BI, Tableau, Matplotlib, or Seaborn.
• Knowledge of data engineering concepts, ETL pipelines, and data integration processes.
• Experience with big data technologies such as Spark, Hadoop, or similar frameworks.
• Hands-on experience with MLOps tools and practices including MLflow, Docker, Kubernetes, CI/CD pipelines, model monitoring, and lifecycle management.
• Experience deploying ML models into production environments.
• Exposure to cloud platforms such as Microsoft Azure and AI-related cloud services.

Responsibilities

• Partner with stakeholders to identify high-value AI/ML opportunities.
• Translate business challenges into data science solutions.
• Design, develop, validate, and deploy ML models for business apps.
• Build scalable AI solutions integrated with enterprise workflows.
• Develop predictive, prescriptive, and optimization models for ops.
• Create data-driven solutions for quality, maintenance, supply chain, and analytics.
• Build reusable ML assets, pipelines, and components.
• Work with structured and unstructured data to develop analytics.
• Deploy models using modern MLOps on cloud platforms.
• Integrate AI with enterprise systems and dashboards.
• Monitor model performance, drift, and business impact.

Skills

Python

ML Algorithms

NLP

DL

CV

SQL

Data Visualization

Spark

MLOps

Azure

CI/CD

Communication

Education

Bachelor's or Master's in CS/DS/AI/Engineering/Math

Tools

Docker

Kubernetes

MLflow

CI/CD

Job description

Autoliv's primary goal is to Save More Lives. Our products never get a second chance. This is why we can never compromise on quality. We are working to increase vehicle safety by developing seatbelts, airbags and steering wheels and you can be part of our team as AI/ML Developer (Data Scientist).

In this role, you will be responsible for designing, developing, and deploying scalable Artificial Intelligence and Machine Learning solutions that create measurable business value across manufacturing, operations, quality, supply chain, and enterprise functions. You will work closely with business leaders and technical teams to transform data into actionable insights, predictive capabilities, and intelligent decision‑making tools.

You will need to deliver production‑ready AI solutions, drive user adoption, and ensure that machine learning initiatives generate tangible business outcomes, including productivity improvements, cost reduction, enhanced quality, and operational efficiency.

Should you be interested in overseeing these tasks and aiming for enhanced performance standards, your role will involve:

• Partnering with business stakeholders to identify and prioritize high‑value AI and Machine Learning opportunities.
• Translating complex business challenges into practical data science and AI solutions.
• Designing, developing, validating, and deploying machine learning models for business‑critical applications.
• Building scalable AI solutions that integrate seamlessly with enterprise applications and workflows.
• Developing predictive, prescriptive, and optimization models to improve operational performance.
• Creating data‑driven solutions for quality improvement, predictive maintenance, supply chain optimization, forecasting, and manufacturing analytics.
• Building reusable machine learning assets, frameworks, pipelines, and model components.
• Working with structured and unstructured datasets to develop robust analytical solutions.
• Deploying machine learning models using modern MLOps practices and cloud‑based platforms.
• Integrating AI solutions through APIs, enterprise systems, dashboards, and business applications.
• Monitoring model performance, accuracy, drift, and business impact throughout the model lifecycle.
• Collaborating with Data Engineering teams to strengthen data pipelines and improve data quality.
• Partnering with IT, Cloud, Infrastructure, and Cybersecurity teams to ensure scalable, secure deployments.
• Creating visualizations and presenting findings in a clear and business‑friendly manner.
• Promoting adoption of AI solutions by building trust, transparency, and stakeholder engagement.
• Driving continuous improvement of models, algorithms, and AI platforms.
• Contributing to enterprise AI standards, best practices, and data science governance.
• Supporting innovation initiatives by investigating emerging AI, ML, and Generative AI technologies.

If you have/are:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Mathematics, or a related field.
• 6+ years of professional experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics.
• Strong knowledge of Machine Learning algorithms including regression, classification, clustering, anomaly detection, and time‑series forecasting.
• Experience with Deep Learning, Natural Language Processing (NLP), Computer Vision, or Generative AI technologies.
• Advanced programming skills in Python and hands‑on experience with libraries such as NumPy, Pandas, Scikit‑learn, TensorFlow, and PyTorch.
• Strong SQL skills and experience working with large‑scale datasets.
• Experience using data visualization tools such as Power BI, Tableau, Matplotlib, or Seaborn.
• Knowledge of data engineering concepts, ETL pipelines, and data integration processes.
• Experience with big data technologies such as Spark, Hadoop, or similar frameworks.
• Hands‑on experience with MLOps tools and practices including MLflow, Docker, Kubernetes, CI/CD pipelines, model monitoring, and lifecycle management.
• Experience deploying machine learning models into production environments.
• Exposure to cloud platforms such as Microsoft Azure and AI‑related cloud services.
• Experience working with enterprise systems and integrating AI capabilities into operational workflows.
• Strong analytical thinking and the ability to solve ambiguous business problems.
• Excellent communication skills with the ability to explain technical concepts to non‑technical audiences.

Key Competencies

• Business‑First Mindset
• Analytical and Data‑Driven Thinking
• Problem Structuring and Critical Thinking
• Machine Learning and Data Science Expertise
• Innovation and Continuous Learning
• Ownership and Accountability
• Collaboration and Stakeholder Management
• AI Solution Architecture
• Model Deployment and MLOps Excellence
• Influencing and Communication Skills
• Customer and User Focus
• Scalability and Platform Thinking
• Decision‑Making Capability
• Results Orientation
• Adaptability and Agility

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate.
• Microsoft Azure Data Science or Machine Learning Certifications.
• Professional Certifications in Artificial Intelligence, Data Science, or Machine Learning.
• Certifications or practical exposure to MLOps frameworks such as MLflow or Kubeflow.
• Experience with Docker, Kubernetes, and CI/CD deployment practices.
• Participation in AI competitions, open‑source projects, research initiatives, or advanced AI programs is highly valued.

What Success Looks Like

• Successfully delivers AI and Machine Learning solutions that create measurable business impact.
• Drives projects from concept and problem definition through deployment and business adoption.
• Develops scalable and reusable solutions rather than isolated proof‑of‑concept models.
• Builds strong partnerships with business leaders and becomes a trusted advisor.
• Successfully bridges business objectives with technical execution.
• Ensures high levels of model performance, reliability, and operational integration.
• Accelerates enterprise AI maturity through innovation and best practices.
• Enables improvements in productivity, quality, forecasting accuracy, equipment reliability, and operational performance.
• Demonstrates continuous learning and brings emerging AI capabilities into practical business applications.