The Domain Architect is responsible for designing and implementing automation frameworks, managing the Software Testing Life Cycle, and collaborating with teams to optimize testing processes.
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.
Responsibilities- 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.
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