AI/ML Engineer – (3-5 Years of experience in AI/ML, Automotive Data, DevOps)
Stellantis
Bengaluru, Karnataka, IndiaMID
AIMLAutomotive
Job Description
AI/ML Engineer at Stellantis focusing on automotive applications.
Responsibilities
- Design, develop, and optimize AI/ML models for automotive use cases such as driver monitoring, predictive analytics, perception, diagnostics, or connected vehicle applications.
- Build and maintain data pipelines for data collection, preprocessing, transformation, validation, and feature engineering from structured and unstructured sources.
- Work on end-to-end model lifecycle activities including training, evaluation, deployment, versioning, and performance monitoring.
- Collaborate with software, data, validation, and platform teams to integrate AI/ML components into production systems.
- Support deployment of AI/ML workloads using DevOps/MLOps practices, including CI/CD, containerization, automated testing, and infrastructure management.
- Develop and maintain scripts, APIs, and services for scalable model serving and batch/stream processing.
- Contribute to developer productivity initiatives by leveraging AI tools for code review, code generation, documentation, test-case generation, defect analysis, and workflow automation.
- Evaluate and integrate AI-assisted engineering tools to improve software development speed, code quality, and release efficiency.
- Ensure data quality, reproducibility, and traceability across datasets, code, and model artifacts.
- Participate in troubleshooting, root-cause analysis, and continuous improvement of deployed AI/ML solutions.
- Contribute to technical documentation, code reviews, and process standardization.
Qualifications
- Around 4 years of experience in AI/ML engineering, preferably in the automotive domain.
- Strong programming skills in Python.
- Good understanding of machine learning and deep learning concepts, including model training, validation, and inference workflows.
- Hands-on experience in building and maintaining data pipelines using tools/frameworks such as Spark, Airflow, Kafka, or similar.
- Exposure to DevOps/MLOps practices, including Docker, Kubernetes, CI/CD pipelines, Git, and cloud/on-prem deployment workflows.
- Experience with data preprocessing, feature engineering, model evaluation, and debugging.
- Familiarity with APIs, microservices, and deployment of AI/ML solutions into production environments.
- Good understanding of software engineering best practices, version control, testing, and documentation.
- Strong problem-solving and analytical skills.
- Ability to work across AI/ML, data engineering, and DevOps domains.
- Good collaboration skills to work with cross-functional engineering teams.
- Strong ownership and ability to independently drive technical tasks.
- Structured communication and documentation skills.
- Ability to learn and adapt to new tools, frameworks, and engineering methods.
- Understanding of AI-assisted software development workflows.
- Experience or exposure to tools for:
- AI-based code review
- code generation / code completion
- unit test generation
- documentation generation
- bug triaging and defect analysis
- PR review automation
- Ability to identify engineering bottlenecks and propose AI-driven productivity improvements.
- Knowledge of integrating AI tools into CI/CD or developer workflows such as GitHub, GitLab, TeamCity, Jenkins, or similar ecosystems.
- Familiarity with using LLM-based tools for:
- improving code quality
- reducing manual effort
- accelerating debugging
- improving developer feedback loops
- Awareness of limitations of AI tools, including: