Top Skills:
Ab InitioAiAirflowAutomationAwsAzureCi/cdCloudComplianceData ArchitectureData ManagementData PipelineEnterpriseGcpGovernanceKafkaKubernetesMicroservicesModel DeploymentMulti-cloudPerformance MonitoringPipelinePythonPytorchScikit-learnSparkSqlTensorflowUnstructured Data
Job Description
This role requires deep expertise in ML engineering practices, cloud-native deployment, and hands-on experience with modern AI platforms. The engineer will be responsible for building scalable ML pipelines, LLM-based applications, and intelligent agent frameworks to accelerate delivery for telecom, enterprise, and next-generation autonomous network solutions.
How You Will Contribute And What You Will Learn
Must-Have:
This role requires deep expertise in ML engineering practices, cloud-native deployment, and hands-on experience with modern AI platforms. The engineer will be responsible for building scalable ML pipelines, LLM-based applications, and intelligent agent frameworks to accelerate delivery for telecom, enterprise, and next-generation autonomous network solutions.
How You Will Contribute And What You Will Learn
- Design, optimize, and scale end-to-end ML pipelines using MLOps best practices, including CI/CD, model deployment, performance monitoring, and governance (drift detection, fairness, compliance).
- Develop and operationalize GenAI/LLM solutions leveraging fine-tuning, prompt engineering, RAG, LLM observability, and integrate agentic AI for autonomous decision-making and workflow orchestration.
- Build and manage robust data pipelines for ingestion, preprocessing, and feature engineering across structured, semi-structured, and unstructured data sources.
- Collaborate with cross-functional teams (data scientists, architects, delivery) to translate use cases into scalable solutions and support PoCs, pilots, and full production rollouts.
- Design, manage, and deploy cloud-native AI/ML infrastructure using platforms like Vertex AI, Red Hat OpenShift AI, and Kubeflow across multi-cloud and hybrid environments with Kubernetes.
- Create reusable accelerators, frameworks, and automation tools to enhance efficiency, reduce time-to-market, and enable scalable AI solution delivery.
Must-Have:
- Bachelor’s/Master’s in Computer Science, Data Engineering, AI/ML, or related field with 10+ years in AI/ML engineering and 5+ years in MLOps.
- Proven experience with LLM/GenAI ecosystems (OpenAI, Anthropic, Vertex AI, Hugging Face, LangChain, LlamaIndex).
- Strong proficiency in Python with ML frameworks (PyTorch, TensorFlow, Scikit-learn) and SQL.
- Expertise in MLOps pipelines and tools (Kubeflow, MLflow, Vertex AI Pipelines, ArgoCD, CI/CD for ML).
- Hands-on experience with data engineering tools (Spark, Kafka, Flink, Airflow).
- Deep knowledge of cloud platforms (GCP, AWS, Azure) and ML pipeline implementation (Vertex AI, OpenShift AI, Kubeflow).
- Experience with Agentic AI frameworks, along with strong skills in APIs, microservices, and distributed systems.
- Familiarity with telecom data products, autonomous networks, and Ab Initio data management platform.
- Experience with modern data architectures (data mesh, data fabric) and vector databases with RAG.
- Understanding of LLM/GenAI security, compliance, governance, and exposure to TM Forum/3GPP or open-source contributions.
Domain Architect
Nokia
linkedin
Noida, Uttar Pradesh, India
10-12 years Not Disclosed
Full time
28 April 2026
Top Skills:
Ab InitioAiAirflowAutomationAwsAzureCi/cdCloudComplianceData ArchitectureData ManagementData PipelineEnterpriseGcpGovernanceKafkaKubernetesMicroservicesModel DeploymentMulti-cloudPerformance MonitoringPipelinePythonPytorchScikit-learnSparkSqlTensorflowUnstructured Data
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Job Description
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Job Description
This role requires deep expertise in ML engineering practices, cloud-native deployment, and hands-on experience with modern AI platforms. The engineer will be responsible for building scalable ML pipelines, LLM-based applications, and intelligent agent frameworks to accelerate delivery for telecom, enterprise, and next-generation autonomous network solutions.
How You Will Contribute And What You Will Learn
Must-Have:
This role requires deep expertise in ML engineering practices, cloud-native deployment, and hands-on experience with modern AI platforms. The engineer will be responsible for building scalable ML pipelines, LLM-based applications, and intelligent agent frameworks to accelerate delivery for telecom, enterprise, and next-generation autonomous network solutions.
How You Will Contribute And What You Will Learn
- Design, optimize, and scale end-to-end ML pipelines using MLOps best practices, including CI/CD, model deployment, performance monitoring, and governance (drift detection, fairness, compliance).
- Develop and operationalize GenAI/LLM solutions leveraging fine-tuning, prompt engineering, RAG, LLM observability, and integrate agentic AI for autonomous decision-making and workflow orchestration.
- Build and manage robust data pipelines for ingestion, preprocessing, and feature engineering across structured, semi-structured, and unstructured data sources.
- Collaborate with cross-functional teams (data scientists, architects, delivery) to translate use cases into scalable solutions and support PoCs, pilots, and full production rollouts.
- Design, manage, and deploy cloud-native AI/ML infrastructure using platforms like Vertex AI, Red Hat OpenShift AI, and Kubeflow across multi-cloud and hybrid environments with Kubernetes.
- Create reusable accelerators, frameworks, and automation tools to enhance efficiency, reduce time-to-market, and enable scalable AI solution delivery.
Must-Have:
- Bachelor’s/Master’s in Computer Science, Data Engineering, AI/ML, or related field with 10+ years in AI/ML engineering and 5+ years in MLOps.
- Proven experience with LLM/GenAI ecosystems (OpenAI, Anthropic, Vertex AI, Hugging Face, LangChain, LlamaIndex).
- Strong proficiency in Python with ML frameworks (PyTorch, TensorFlow, Scikit-learn) and SQL.
- Expertise in MLOps pipelines and tools (Kubeflow, MLflow, Vertex AI Pipelines, ArgoCD, CI/CD for ML).
- Hands-on experience with data engineering tools (Spark, Kafka, Flink, Airflow).
- Deep knowledge of cloud platforms (GCP, AWS, Azure) and ML pipeline implementation (Vertex AI, OpenShift AI, Kubeflow).
- Experience with Agentic AI frameworks, along with strong skills in APIs, microservices, and distributed systems.
- Familiarity with telecom data products, autonomous networks, and Ab Initio data management platform.
- Experience with modern data architectures (data mesh, data fabric) and vector databases with RAG.
- Understanding of LLM/GenAI security, compliance, governance, and exposure to TM Forum/3GPP or open-source contributions.