Position Overview The Senior AI Engineer is the intersection of world-class engineering and cutting-edge artificial intelligence. This role leads the design, development, and production deployment of advanced ML models, computer vision systems, large language model workflows, and scalable AI pipelines. You will architect agentic automation workflows, implement retrieval-first RAG systems with grounded and citation-aware outputs, and enforce permission-sensitive context routing across vector retrieval systems. You deeply understand hallucination control, context budgeting, prompt regression testing, model evaluation, and cost-per-answer optimization—and you solve them pragmatically in production environments. This role requires hands-on expertise, research literacy, and the ability to mentor teams while accelerating innovation across Presight’s AI ecosystem. Key Responsibilities • Design, train, evaluate, and deploy AI/ML models across NLP, computer vision, and multimodal domains. • Build retrieval-first RAG architectures, including advanced chunking strategies, hybrid retrieval, reranking, grounding, and citation discipline. • Architect and optimize scalable agentic workflows, vector search pipelines, and real-time inference systems. • Integrate AI models into production systems with performance, observability, scalability, and security designed in. • Develop and maintain an AI tool ecosystem supporting repository analysis, doc parsing, content inspection, and search primitives. • Lead rapid prototyping efforts; evaluate emerging models, frameworks, and agentic platforms and define adoption roadmaps. • Design evaluation frameworks, track model regressions, and optimize hallucination control and cost-to-serve metrics. • Produce high-quality documentation covering model behavior, dataset lineage, methodologies, and evaluation outcomes. • Partner cross-functionally with solution engineering, software teams, and product owners to align AI capabilities with customer impact. • Mentor junior engineers and champion best practices for AI development, testing, ethical use, and model lifecycle governance. • Comply with QHSE (Quality Health Safety and Environment), Business Continuity, Information Security, Privacy, Risk, Compliance Management and Governance of Organizations policies, procedures, plans and related risk assessments. Requirements • Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or related field. • 6–10+ years in engineering or data science; minimum 3 years deploying AI/RAG systems in production (not prototype-only). • Expertise in deep learning architectures, computer vision, NLP, or multimodal model stacks. • Strong proficiency in Python and deep learning frameworks (PyTorch or Tensor Flow). • Hands-on experience with RAG pipelines, vector databases, LLM fine-tuning, and MLOps automation. • Experience with cloud platforms (Azure, AWS, or GCP) and containerized deployments. • Demonstrated capability mentoring engineers or leading complex AI delivery projects. • Excellent analytical, communication, and technical narrative skills for stakeholder alignment.