Sr Machine Learning Engineer

Role & responsibilities

• Design and implement AI/ML models for anomaly detection, forecasting, and incident prediction.
• Develop and optimize MLOps pipelines for scalable, automated model training, validation, deployment, and monitoring.
• Select and evaluate appropriate algorithms and statistical methods for diverse telecom data sets.
• Apply best practices for data preprocessing, feature engineering, and model interpretability.
• Collaborate with software architects and engineering teams to integrate ML models into a cloud-native, microservices-based platform.
• Contribute to the selection and integration of MLOps tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
• Ensure data quality, governance, and compliance across the ML lifecycle.
• Stay current with emerging trends in AI/ML, MLOps, and telecom analytics to inform technical decisions.
• Leverage LLMs for software development workflows including automated code generation, documentation, code review, and test-case synthesis using tools like OpenAI Codex, Claude Code, or Gemini Code Assist.
• Explore and build Agentic AI systems capable of autonomous reasoning, task planning, and multi-step decision-making for enterprise automation.
• LLM Fine-Tuning & Prompt Engineering optimizing prompts for models like GPT, Claude, Llama, or Mistral.
• Knowledge Augmented Generation (RAG) – integrating vector databases (e.g., FAISS, Pinecone, Chroma). Implement Retrieval-Augmented Generation (RAG) pipelines using LLMs and vector databases.
• GenAI Model APIs – hands-on with OpenAI API, Hugging Face Transformers, LangChain, LlamaIndex.
• Ethical AI Awareness – bias detection, explainability, and responsible GenAI deployment.
• Stay updated with latest trends in AI, GenAI, and large language models, bringing innovative ideas into the organization

Preferred:

• Experience with AI observability, model drift detection, and continuous learning systems.
• Background in telecom assurance or AIOps platforms.
• Knowledge of TMF standards and SaaS architectures.
• Contributions to open-source MLOps or data science tools, GenAI projects or AI hackathons.