Sr. Developer
VOLKSWAGEN GROUP DIGITAL SOLUTIONS PRIVATE LIMITED · Pune · onsite · Posted 35d ago
Undisclosed6–10 yrsfulltimeSourced from
About a minute. You'll make an account on the way through.
About this role
Sr. Developer
Skills - AI-ML , Python , ML/NLP frameworks (PyTorch, TensorFlow, Hugging Face, etc.). , Cloud Platforms(AWS, Azure or GCP) ,
Monitoring/observability tools (Prometheus, Grafana, ELK) 6-10 years of progressive work experience in IT
Hand-on experience in AI/ML engineering, plus experience in SRE, DevOps, or production operations.
Strong programming skills in Python (plus Go/Java is a bonus).
Experience with ML/NLP frameworks (PyTorch, TensorFlow, Hugging Face, etc.).
Experience with distributed systems, APIs, and microservices.
Hands-on experience with monitoring/observability tools (Prometheus, Grafana, ELK).
Familiarity with cloud platforms (AWS, Azure, or GCP).
Experience building AI copilots or autonomous agents for IT operations (AIOps).
Knowledge of LLMs, retrieval-augmented generation (RAG), and embeddings.
Experience with event-driven architectures and streaming platforms (Kafka).
Familiarity with runbook automation and infrastructure-as-code tools.
Background in high-scale production environments.
Monitoring/observability tools (Prometheus, Grafana, ELK) 6-10 years of progressive work experience in IT
Hand-on experience in AI/ML engineering, plus experience in SRE, DevOps, or production operations.
Strong programming skills in Python (plus Go/Java is a bonus).
Experience with ML/NLP frameworks (PyTorch, TensorFlow, Hugging Face, etc.).
Experience with distributed systems, APIs, and microservices.
Hands-on experience with monitoring/observability tools (Prometheus, Grafana, ELK).
Familiarity with cloud platforms (AWS, Azure, or GCP).
Experience building AI copilots or autonomous agents for IT operations (AIOps).
Knowledge of LLMs, retrieval-augmented generation (RAG), and embeddings.
Experience with event-driven architectures and streaming platforms (Kafka).
Familiarity with runbook automation and infrastructure-as-code tools.
Background in high-scale production environments.
Key Responsibilities Tasks:
AI-Driven Toil Elimination - Build AI agents and models to automate alert triage, deduplication, and prioritization.
Develop intelligent ticket classification, routing, and auto-resolution systems.
Design NLP-based systems to interpret logs, runbooks, and incident history. Implement AI-powered root cause analysis and anomaly detection.
Create self-healing systems that automatically execute remediation actions.
SRE for AI Systems - Ensure high reliability and availability of automation systems handling production incidents.
Define SLIs/SLOs specifically for automated resolution systems (e.g., auto-resolve rate, false positives). Design safe automation with guardrails, fallbacks, and human-in-the-loop controls.
Implement robust incident response pipelines integrating AI decision-making.
Conduct post-incident reviews focusing on further automation opportunities.
Operational Intelligence Platform - Build unified observability pipelines (logs, metrics, traces) for AI consumption.
Develop knowledge graphs or embeddings from historical incidents and runbooks. Integrate AI systems with ITSM tools (ServiceNow, Jira, PagerDuty, incident.io etc.).
Enable continuous learning systems that improve from past incidents."
AI-Driven Toil Elimination - Build AI agents and models to automate alert triage, deduplication, and prioritization.
Develop intelligent ticket classification, routing, and auto-resolution systems.
Design NLP-based systems to interpret logs, runbooks, and incident history. Implement AI-powered root cause analysis and anomaly detection.
Create self-healing systems that automatically execute remediation actions.
SRE for AI Systems - Ensure high reliability and availability of automation systems handling production incidents.
Define SLIs/SLOs specifically for automated resolution systems (e.g., auto-resolve rate, false positives). Design safe automation with guardrails, fallbacks, and human-in-the-loop controls.
Implement robust incident response pipelines integrating AI decision-making.
Conduct post-incident reviews focusing on further automation opportunities.
Operational Intelligence Platform - Build unified observability pipelines (logs, metrics, traces) for AI consumption.
Develop knowledge graphs or embeddings from historical incidents and runbooks. Integrate AI systems with ITSM tools (ServiceNow, Jira, PagerDuty, incident.io etc.).
Enable continuous learning systems that improve from past incidents."
Sourced from naukri
Skills
AutomationDevopsGrafanait operationsitsmAIOpsApache KafkaDistributed Systemscloud platformsapisIncident Responsegoogle cloud platformamazon web servicesclassificationAutomation systemsitsm toolsEmbeddingsAnomaly DetectionJira AtlassiancloudJavaMicrosoft AzureArtificial Intelligence