Lead AI Systems&LLMs for Scale (Remote)

<p>AlgoLand is a global AI advisory and product company specializing in applied artificial intelligence, advanced analytics, and scalable AI systems. We partner with enterprises and governments to design, build, and deploy AI solutions that generate measurable business impact. Our work spans optimization, machine learning, and next-generation AI systems, including large language models (LLMs) and autonomous agents.</p><p>We operate at the intersection of science and business, translating cutting-edge research into production-grade systems used in real-world environments. Our culture emphasizes technical excellence, ownership, and outcome-driven delivery.</p><p><strong>Role Overview</strong></p><p>We are seeking a Lead Applied AI Scientist to drive the design, development, and deployment of AI solutions at scale. This is a senior, hands-on leadership role combining deep technical expertise with system-level thinking and product impact.</p><p>You will lead end-to-end AI initiatives—from problem framing and modeling to deployment and continuous optimization—across a range of use cases including predictive modeling, optimization, and LLM-based systems.</p><p>This is a fully remote role, operating within a distributed team. Availability in or close to GCC time zones is strongly preferred to ensure alignment with clients and delivery teams.</p><p><strong>Key Responsibilities</strong></p><p>1. AI System Design&Development</p><ul><li>Lead the design and implementation of advanced AI models across domains (forecasting, optimization, classification, recommendation, etc.)</li><li>Architect scalable AI pipelines, from data ingestion to model serving</li><li>Apply statistical modeling, machine learning, and deep learning techniques to solve complex business problems</li><li>Translate ambiguous business challenges into structured AI solutions with clear success criteria</li></ul><p>2. LLMs and Next-Gen AI Systems</p><ul><li>Design and deploy LLM-based solutions (RAG pipelines, fine-tuning, prompt engineering, agentic workflows)</li><li>Evaluate and optimize LLM performance (latency, accuracy, cost, hallucination control)</li><li>Build domain-specific AI systems leveraging proprietary data and custom models</li><li>Stay at the forefront of advancements in generative AI and integrate them into production systems</li><li>Lead deployment of AI models into production environments at scale (cloud or on-prem)</li><li>Define and implement MLOps best practices (CI/CD, monitoring, retraining, versioning)</li><li>Ensure reliability, scalability, and performance of AI systems (latency, throughput, robustness)</li><li>Work closely with engineering teams to integrate models into business workflows and applications</li></ul><p>4. Technical Leadership</p><ul><li>Provide technical direction and mentorship to AI scientists and engineers</li><li>Establish standards for model development, evaluation, and deployment</li><li>Review and challenge model design, assumptions, and methodologies to ensure rigor</li><li>Contribute to building reusable frameworks, accelerators, and internal tooling</li></ul><p>5. Client&Stakeholder Engagement</p><ul><li>Collaborate with business stakeholders to define AI use cases and expected impact</li><li>Communicate complex AI concepts clearly to non-technical audiences (executives, product teams)</li><li>Support proposal development, solution design, and technical positioning in client engagements</li></ul><p><strong>QualificationsRequired</strong></p><ul><li>Advanced degree (Master’s or PhD) in Computer Science, Data Science, Applied Mathematics, or related field</li><li>6–10+ years of experience in applied AI / machine learning roles, with demonstrated production impact</li><li>Strong expertise in:</li><li>Machine Learning and Statistical Modeling</li><li>Data modeling and feature engineering</li><li>Proven experience deploying AI models into production at scale</li><li>Strong programming skills (Python required; additional languages are a plus)</li><li>Experience with modern data and ML ecosystems (e.g., MLflow, Docker, Kubernetes, cloud platforms)</li><li>Solid understanding of model evaluation, validation, and performance monitoring</li><li>Basic understanding of Cloud technologies</li></ul><p><strong>Preferred</strong></p><ul><li>Experience with LLMs and Generative AI systems (RAG, fine-tuning, embeddings, agents)</li><li>Experience in optimization / operations research is a strong plus</li><li>Familiarity with distributed systems and high-scale data pipelines</li><li>Exposure to multiple industries (retail, telecom, finance, etc.)</li><li>Experience working in consulting or client-facing environments</li><li>GCP knowledge</li><li>Strong problem-solving ability with a structured, scientific approach</li><li>Ability to operate autonomously in a remote, distributed environment</li><li>Ownership mindset: from concept to production</li><li>High standards of rigor, with attention to reproducibility and reliabil