Role : AWS Cloud Engineer for Automotive Data Science, AI & GenAI Expert

Role and Responsibilities :

- Design, develop, and implement deployment pipelines for Data Science, ML, AI, and GenAI solutions on AWS cloud.

- Build and maintain CI/CD and CT pipelines using GitHub Actions, Airflow, or similar orchestration tools.

- Support deployment and lifecycle management of ML models, LLM-based applications, prompt workflows, embeddings, vector search, and API-based AI services.

- Collaborate with data scientists, GenAI engineers, and data engineers to understand technical requirements, solution design, and deployment processes; document standards and operating procedures.

- Continuously monitor and maintain ML and GenAI pipelines in production, ensuring performance, reliability, latency, cost efficiency, and model quality.

- Implement observability for AI/GenAI workloads, including model drift, data quality, prompt quality, hallucination indicators, latency, throughput, and cost metrics.

- Optimize pipelines and runtime environments for scalability, security, automation, and cost-effectiveness.

- Troubleshoot and resolve issues related to deployments, integrations, production incidents, and model/service performance.

- Ensure compliance with security, privacy, responsible AI, and data governance standards across all deployment activities.

- Support experimentation and release processes for model versions, prompt versions, feature pipelines, and evaluation workflows.

- Keep up to date with emerging tools, best practices, and trends in AI Ops, MLOps, and LLMOps.

- Provide support, guidance, and knowledge sharing to other team members on deployment, automation, monitoring, and operational best practices.

Education and Competencies :

- Bachelor's or master's degree in computer science, engineering, informatics, data science, or equivalent qualification.

- Strong hands-on experience in Data Science, AI, and GenAI operations with AWS cloud platform services such as ECS, SageMaker, Batch, Lambda, API Gateway, S3, Redshift, CloudWatch, and related managed services.

- Experience with GenAI ecosystem components such as foundation models, prompt orchestration, retrieval-augmented generation, vector databases, model gateways, and evaluation/monitoring frameworks.

- Proficiency in Python and PySpark, with hands-on experience in containers, Airflow, GitHub Actions, SonarQube, and related automation/tooling stacks.

- Strong understanding of CI/CD, deployment automation, infrastructure as code, and production monitoring tools such as Datadog or equivalent observability platforms.

- Experience with MLOps, LLMOps, and AI lifecycle management in enterprise environments.

- Ability to understand architectural and technical dependencies in customer analytics and AI environments.

- Strong ability to translate business and technical requirements into scalable technical implementations.

- Confident communicator who can present effectively internally and with clients.

- Experience working in Agile delivery models.

- Strong team player who can coordinate effectively across distributed, global teams and time zones.