Lead R&D Engineer

Our team significantly contributes to innovative research and development projects at the intersection of science, engineering, and creativity. We aim at designing transformative AI solutions that impact various application domains.

• 7+ years of hands-on experience in AI/ML, including 3+ years leading R&D and production-ready projects
• PhD or Master’s in Machine Learning, Computer Science, or related fields, or equivalent senior industry experience
• Recognized specialist in NLP and LLMs, with experience in training, fine-tuning, retrieval-augmented generation (RAG), and large-scale evaluation
• Experience designing and deploying Agentic AI systems, including multi-agent orchestration, tool integrations, and autonomous workflows
• Highly proficient in Python, with additional skills in C++/Java for optimizing performance-critical components
• Deep familiarity with PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, and related NLP/LLM frameworks
• Skilled in working with vector databases and retrieval systems for real-time knowledge grounding in AI agents
• Experience in cloud architectures (AWS, GCP, Azure) and distributed training for large models, including quantization and fine-tuning for deployment efficiency
• Knowledgeable in MLOps practices, including CI/CD pipelines for ML, Docker/Kubernetes orchestration, and monitoring/model lifecycle management
• Leadership skills to mentor cross-functional ML/AI teams and align research with organizational priorities
• Ability to present complex AI/ML concepts to executives, product managers, or external stakeholders clearly
• Published or contributed to research, patents, or open-source projects in NLP, LLMs, reinforcement learning, or multi-agent AI systems
• Awareness and commitment to AI ethics, fairness, bias mitigation, and data security within generative and agentic AI applications

• Define and execute the R&D roadmap for advanced NLP, LLM, and Agentic AI systems
• Lead the development of novel architectures for multi-agent orchestration, tool-use integration, and retrieval-augmented generation
• Drive collaboration with product, engineering, and research teams to align AI innovation with business impact
• Architect scalable training, fine-tuning, and deployment workflows on cloud and distributed systems
• Establish best practices in MLOps, model evaluation, and responsible AI for production-grade deployments
• Mentor and grow a high-performing team of AI engineers and researchers
• Communicate progress, technical insights, and breakthroughs to executives and stakeholders