{"id":1444988,"url":"https://alion.io/job/aziro-aws-cloud-engineer","title":"AWS Cloud Engineer","company":{"id":3800826,"name":"Aziro","domain":"aziro.com","url":"https://alion.io/company/aziro","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Pune, India","Chennai, 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Actions","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"LLMOps","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SonarQube","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-29T05:11:47Z","employer_posted_date":null,"last_verified_at":"2026-09-29T05:11:47Z","board_verified":false,"closed_at":null,"days_open":16,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":16},"description":"Role : AWS Cloud Engineer for Automotive Data Science, AI & GenAI Expert\n\nRole and Responsibilities :\n\n- Design, develop, and implement deployment pipelines for Data Science, ML, AI, and GenAI solutions on AWS cloud.\n\n- Build and maintain CI/CD and CT pipelines using GitHub Actions, Airflow, or similar orchestration tools.\n\n- Support deployment and lifecycle management of ML models, LLM-based applications, prompt workflows, embeddings, vector search, and API-based AI services.\n\n- Collaborate with data scientists, GenAI engineers, and data engineers to understand technical requirements, solution design, and deployment processes; document standards and operating procedures.\n\n- Continuously monitor and maintain ML and GenAI pipelines in production, ensuring performance, reliability, latency, cost efficiency, and model quality.\n\n- Implement observability for AI/GenAI workloads, including model drift, data quality, prompt quality, hallucination indicators, latency, throughput, and cost metrics.\n\n- Optimize pipelines and runtime environments for scalability, security, automation, and cost-effectiveness.\n\n- Troubleshoot and resolve issues related to deployments, integrations, production incidents, and model/service performance.\n\n- Ensure compliance with security, privacy, responsible AI, and data governance standards across all deployment activities.\n\n- Support experimentation and release processes for model versions, prompt versions, feature pipelines, and evaluation workflows.\n\n- Keep up to date with emerging tools, best practices, and trends in AI Ops, MLOps, and LLMOps.\n\n- Provide support, guidance, and knowledge sharing to other team members on deployment, automation, monitoring, and operational best practices.\n\nEducation and Competencies :\n\n- Bachelor's or master's degree in computer science, engineering, informatics, data science, or equivalent qualification.\n\n- 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.\n\n- Experience with GenAI ecosystem components such as foundation models, prompt orchestration, retrieval-augmented generation, vector databases, model gateways, and evaluation/monitoring frameworks.\n\n- Proficiency in Python and PySpark, with hands-on experience in containers, Airflow, GitHub Actions, SonarQube, and related automation/tooling stacks.\n\n- Strong understanding of CI/CD, deployment automation, infrastructure as code, and production monitoring tools such as Datadog or equivalent observability platforms.\n\n- Experience with MLOps, LLMOps, and AI lifecycle management in enterprise environments.\n\n- Ability to understand architectural and technical dependencies in customer analytics and AI environments.\n\n- Strong ability to translate business and technical requirements into scalable technical implementations.\n\n- Confident communicator who can present effectively internally and with clients.\n\n- Experience working in Agile delivery models.\n\n- Strong team player who can coordinate effectively across distributed, global teams and time zones.\nSkills\nAWS, Cloud, Cloud Services, CI/CD, Github, Python, PySpark, Datadog, Monitoring Tools, LLMOps, 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