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Job Post Details
Job Title: AI/ML Engineer – Automotive Data, DevOps & Developer Productivity - job post
Job details
Pay
- ₹4,56,646.56 - ₹17,58,087.79 a year
Job type
- Contractual / Temporary
- Full-time
Location
Benefits
Pulled from the full job description
- Flexible schedule
Full job description
Bachelor’s or Master’s Degree in Computer Science, Electronics, Artificial Intelligence, Data Science, Automotive Engineering, or related fields.
HR Point of Contact-
Ms. Soni Mohite , Contact No.: 9665668200
- Bangalore, MNC Company
We are looking for an AI/ML Engineer with around 6 years of experience, preferably in the Automotive domain, to support the development, deployment, and maintenance of AI/ML solutions for connected, embedded, and vehicle-related applications. The ideal candidate should have good hands-on experience in Machine Learning, Data Engineering, Data Pipelines, and DevOps/MLOps practices, along with exposure to AI-based developer productivity tools and automation methods.
The candidate will be responsible for designing, developing, and optimizing AI/ML models for automotive applications such as Driver Monitoring Systems, Predictive Analytics, Perception Systems, Vehicle Diagnostics, and Connected Vehicle Applications. The role also includes building and maintaining data pipelines for data collection, pre-processing, transformation, validation, and feature engineering from structured and unstructured data sources.
The candidate will work on complete machine learning lifecycle activities including model training, validation, evaluation, deployment, versioning, and performance monitoring. Coordination with software, platform, validation, and data engineering teams will be required for integration of AI/ML solutions into production systems.
The role also involves supporting AI/ML deployment using DevOps/MLOps practices such as CI/CD pipelines, Docker, Kubernetes, automated testing, and infrastructure management. The candidate will develop scripts, APIs, and scalable services for model serving, batch processing, and stream processing.
In addition, the candidate will contribute to developer productivity improvement initiatives using AI tools for code review, code generation, documentation generation, unit test generation, bug analysis, PR review automation, and workflow automation. The candidate should also support evaluation and integration of AI-assisted engineering tools to improve software development speed, code quality, and release efficiency.
Candidates should have around 4 years of experience in AI/ML Engineering with strong programming knowledge in Python. Good understanding of Machine Learning, Deep Learning, model training, validation, and inference workflows is required.
Hands-on experience with data pipeline tools such as Spark, Airflow, Kafka, or similar technologies is preferred. Exposure to DevOps/MLOps tools and practices including Docker, Kubernetes, Git, CI/CD pipelines, and cloud or on-prem deployment environments is required.
The candidate should have experience in data pre-processing, feature engineering, model evaluation, debugging, APIs, micro services, and deployment of AI/ML solutions into production environments. Good understanding of software engineering practices, version control, testing, and technical documentation is expected.
The ideal candidate should have understanding of AI-assisted software development workflows and tools related to AI-based code review, code generation, unit test generation, documentation generation, bug triaging, defect analysis, and PR review automation.
Experience or exposure to AI productivity tools such as GitHub Copilot, Claude, Cursor, Codeium, AI PR Reviewers, or similar platforms will be preferred. Knowledge of integrating AI tools into developer workflows using GitHub, GitLab, Jenkins, TeamCity, or similar platforms is an advantage.
Candidates should also understand AI limitations such as hallucination risks, context limitations, code privacy, security concerns, and validation requirements before production deployment. Ability to improve engineering productivity, reduce manual effort, improve code quality, and accelerate debugging will be highly valued.
Experience in automotive technologies such as ADAS, Autonomous Driving, Driver Monitoring Systems, Cockpit AI, Vehicle Diagnostics, and Connected Vehicle Systems will be preferred. Exposure to PyTorch, TensorFlow, ONNX, OpenCV, MLflow, Databricks, Embedded AI, Edge AI deployment, and cloud platforms such as AWS, Azure, or GCP will be an added advantage.
Pay: ₹456,646.56 - ₹1,758,087.79 per year
Benefits:
- Flexible schedule
Work Location: In person