Job Title: AI/ML Engineer – Automotive Data, DevOps & Developer Productivity
We are looking for an AI/ML Engineer with around 4 years of experience, preferably in the automotive domain, to support the design, development, deployment, and maintenance of AI/ML solutions for connected, embedded, or vehicle-related applications. The ideal candidate should have hands-on experience in machine learning, data pipelines, and DevOps/MLOps practices, along with exposure to AI-driven developer productivity tools and methods to improve engineering efficiency, code quality, and automation.
Key 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.
Required Skills and Experience
- 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.
Competencies Required
- 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.
Additional Competencies for Improving Developer Productivity Using AI Tools and 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:
- hallucination risk
- context limitations
- code privacy and security concerns
- validation requirements before production use
- Ability to define metrics for developer productivity improvement, such as:
- reduced PR review time
- improved unit test coverage
- faster root-cause analysis
- reduced manual documentation effort
- improved code quality consistency
Preferred Skills
- Experience in automotive domains such as ADAS, autonomous driving, driver monitoring, cockpit AI, vehicle diagnostics, or connected vehicle systems.
- Exposure to frameworks such as PyTorch, TensorFlow, ONNX, or OpenCV.
- Knowledge of MLOps tools like MLflow, Databricks, or model registry solutions.
- Understanding of embedded or edge AI deployment is an added advantage.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Experience working in Agile teams and cross-functional product environments.
- Exposure to developer productivity tools such as Claude, GitHub Copilot, Codeium, Cursor, AI PR reviewers, or similar platforms.
Education
- Bachelor's or Master's degree in Computer Science, Electronics, Data Science, Artificial Intelligence, Automotive Engineering, or related field.
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