Artificial Intelligence Researcher

Role DetailsExperience: 4–6 years, Research Scholar, AuthorPrimary Tech/Domain: Python, ML, LLMsRole SummaryLead and deliver high impact research in AI and data. Own execution excellence with measurable business value, technical depth, and governance. Responsibilities: Novel algorithms or architectures – Development of new techniques for LLMs, agentic AI, or generative models that outperform existing baselines.Published research & thought leadership – Peer-reviewed papers, whitepapers, or technical blogs showcasing innovation and advancing industry knowledge.Prototype systems & proof-of-concepts – Working demos of new AI capabilities validated for feasibility and scalability.Performance benchmarks & evaluation frameworks – Comprehensive metrics for accuracy, latency, cost, fairness, and robustness across models.Open-source contributions & tools – Release of reusable libraries, datasets, or frameworks to accelerate adoption and community engagement. Technical Skills: HF Transformers: Trainer API, fine-tuning, LoRA/PEFT; evaluation Experimentation: notebooks, MLflow logging, reproducibility LLMOps: RAG baselines, prompt optimization, benchmarks Optimization: quantization (bitsandbytes), distillation, throughput/latency Data: curation, augmentation, synthetic data caveats Publishing: experiment reports, ablations, open-source contributions, publishing research papers. Architecture & Tooling Stack: Source control & workflow: Git, branching standards, PR reviews, trunk-based delivery. Containers & orchestration: Docker, Kubernetes, Helm; secrets, configs, RBAC. Observability: logs, metrics, traces; dashboards with alerting & on-call runbooks. Data/Model registries: metadata, lineage, versioning; staged promotions. Qualifications: Bachelor’s/Master’s in CS/CE/EE/Data Science or equivalent practical experience. Strong applied programming in Python; familiarity with modern data/ML ecosystems. Proven track record of shipping and operating systems in production.