Domain Architect

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.

• 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.

Nice-To -Have

• 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.