- Company Name
- Aduna Global
- Job Title
- Artificial Intelligence Researcher
- Job Description
-
Job title: Artificial Intelligence Researcher
Role Summary: Apply machine learning and AI research to design, prototype, and deploy production‑ready models for automation, intelligence, and API services within a telecom‑centric environment. Focus on large‑language models, graph neural networks, multi‑agent orchestration, and Retrieval Augmented Generation pipelines.
Expectations:
- 5+ years of applied AI/ML research with measurable deployments.
- Strong programming in Python and experience with Scikit‑learn, PyTorch, TensorFlow.
- Deep understanding of NLP, LLMs, GNNs, or agent‑based systems.
- Proficient with ML lifecycle tools (MLflow, Weights & Biases), Git, and production‑grade deployment practices.
Key Responsibilities:
- Design, prototype, and deploy AI models for automation, analytics, and intelligent API services.
- Conduct applied research using LLMs, GNNs, Transformers, and graph reasoning in software, cybersecurity, or network contexts.
- Build RAG systems and related pipelines (embeddings, vector search, retrieval orchestration).
- Collaborate with MLOps, engineering, and infra teams to move models into production.
- Adapt and fine‑tune open‑source or proprietary models for multimodal structured, unstructured, or graph data.
- Contribute to AI architecture ensuring scalability, auditability, ethics, and security.
- Evaluate models for performance, robustness, interpretability, and drift; perform error analysis and tuning.
- Document best practices and share knowledge across teams.
Required Skills:
- Python programming; libraries: Scikit‑learn, PyTorch, TensorFlow.
- Expertise in NLP, LLMs, GNNs, or agent‑based systems.
- Experience with ML lifecycle tools (MLflow, Weights & Biases).
- Git proficiency and familiarity with CI/CD for ML deployments.
- Knowledge of structured data (graphs, logs, APIs) and unstructured data (text, code).
- Strong evaluation, error analysis, and hyper‑parameter tuning capabilities.
Required Education & Certifications:
- Master’s or Ph.D. in Computer Science, Engineering, or a related field.
Nice‑to‑Have:
- Code intelligence or AI for developers exposure.
- Telecom data, API, or security domain knowledge.
- Experience with LangChain, AutoGen, or other multi‑agent systems.
- Familiarity with federated learning, privacy, explainability, AI governance, or RLHF.
- Open‑source contributions or research publications.