Lead Applied AI Scientist - Remote

Company Description

Role Overview

1. AI System Design & Development

• Lead the design and implementation of advanced AI models across domains (forecasting, optimization, classification, recommendation, etc.)
• Architect scalable AI pipelines, from data ingestion to model serving
• Apply statistical modeling, machine learning, and deep learning techniques to solve complex business problems
• Translate ambiguous business challenges into structured AI solutions with clear success criteria

2. LLMs and Next-Gen AI Systems

• Design and deploy LLM-based solutions (RAG pipelines, fine-tuning, prompt engineering, agentic workflows)
• Evaluate and optimize LLM performance (latency, accuracy, cost, hallucination control)
• Build domain-specific AI systems leveraging proprietary data and custom models
• Stay at the forefront of advancements in generative AI and integrate them into production systems

3. Productionization & MLOps

• Lead deployment of AI models into production environments at scale (cloud or on-prem)
• Define and implement MLOps best practices (CI/CD, monitoring, retraining, versioning)
• Ensure reliability, scalability, and performance of AI systems (latency, throughput, robustness)
• Work closely with engineering teams to integrate models into business workflows and applications

4. Technical Leadership

• Provide technical direction and mentorship to AI scientists and engineers
• Establish standards for model development, evaluation, and deployment
• Review and challenge model design, assumptions, and methodologies to ensure rigor
• Contribute to building reusable frameworks, accelerators, and internal tooling

5. Client & Stakeholder Engagement

• Collaborate with business stakeholders to define AI use cases and expected impact
• Communicate complex AI concepts clearly to non-technical audiences (executives, product teams)
• Support proposal development, solution design, and technical positioning in client engagements

QualificationsRequired

• Advanced degree (Master’s or PhD) in Computer Science, Data Science, Applied Mathematics, or related field
• 6–10+ years of experience in applied AI / machine learning roles, with demonstrated production impact
• Strong expertise in:
• Machine Learning and Statistical Modeling
• Data modeling and feature engineering
• Proven experience deploying AI models into production at scale
• Strong programming skills (Python required; additional languages are a plus)
• Experience with modern data and ML ecosystems (e.g., MLflow, Docker, Kubernetes, cloud platforms)
• Solid understanding of model evaluation, validation, and performance monitoring
• Basic understanding of Cloud technologies

Preferred

• Experience with LLMs and Generative AI systems (RAG, fine-tuning, embeddings, agents)
• Experience in optimization / operations research is a strong plus
• Familiarity with distributed systems and high-scale data pipelines
• Exposure to multiple industries (retail, telecom, finance, etc.)
• Experience working in consulting or client-facing environments
• GCP knowledge

Key Competencies

• Strong problem-solving ability with a structured, scientific approach
• Ability to operate autonomously in a remote, distributed environment
• Ownership mindset: from concept to production
• High standards of rigor, with attention to reproducibility and reliability
• Clear and concise communication, both technical and business-oriented

Work Environment

• Fully remote, globally distributed team
• High degree of autonomy and ownership expected
• Async-first collaboration culture with strong documentation practices
• Fast-paced, outcome-driven environment focused on real-world impact
• Preference for candidates aligned with GCC time zones to support client collaboration

What We Offer

• Opportunity to work on high-impact AI problems across industries
• Exposure to cutting-edge AI technologies, including LLMs and advanced optimization
• A culture of technical excellence and continuous learning
• Flexible remote work environment with global collaboration
• Competitive compensation aligned with experience and impact

Candidate Differentiators

• Candidates will stand out if they demonstrate:
• Proven deployment of AI systems in production at scale with measurable impact
• Hands-on experience with LLMs (RAG, fine-tuning, agents) in real-world use cases
• End-to-end ownership (from problem framing to deployment and monitoring)
• Strong MLOps and system design capabilities (CI/CD, scaling, reliability)
• Ability to translate business problems into impact-driven AI solutions
• Experience working autonomously in remote, distributed teams

Hiring Process

• Screening Call (30 min) – Background, experience, and role alignment
• Technical Interview (60 min) – Deep dive into past projects and system design
• Case Study (Take-home or Live) – End-to-end AI/LLM solution design
• Final Interview (45 min) – Leadership, collaboration, and team fit
• Decision Criteria: Ability to design, deploy, and scale AI systems delivering real business value.
• AlgoLand is building the next generation of applied AI systems.
• This role is for individuals who want to move beyond experimentation and deliver AI that works in the real world, at scale.