AI/ML Engineer (3-5 Years of experience in AI/ML, Automotive Data, DevOps)

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 Job Titl