AI Forward Engineer

# AI Forward Engineer

About the Role

GoMeasure Platform is seeking a talented AI Forward Engineer to join our team. In this role, you will be at the forefront of designing, implementing, and optimizing AI-driven solutions that power our platform. You will work closely with product, engineering, and data science teams to build scalable AI systems that deliver measurable value to our users. This position requires a blend of strong software engineering fundamentals, deep understanding of modern AI/ML techniques, and the ability to rapidly prototype and deploy production-ready solutions.

Key Responsibilities

AI System Development

• Design and implement AI-powered features and capabilities within the GoMeasure Platform
• Build and maintain robust ML pipelines for training, evaluation, and deployment
• Optimize model performance, latency, and resource utilization for production environments
• Integrate large language models (LLMs) and other foundation models into platform workflows
• Develop prompt engineering strategies and fine-tuning approaches for domain-specific applications

Platform Engineering

• Architect scalable backend services to support AI/ML workloads
• Implement monitoring, logging, and observability solutions for AI systems
• Build APIs and interfaces that expose AI capabilities to other platform components
• Ensure reliability, security, and compliance of AI-driven features
• Collaborate with DevOps to establish CI/CD pipelines for ML models

Research & Innovation

• Stay current with emerging AI technologies, frameworks, and best practices
• Evaluate and prototype new AI techniques that could benefit the platform
• Conduct experiments to validate hypotheses and measure impact
• Share knowledge and insights with the broader engineering team
• Contribute to technical documentation and architectural decision records

Required Qualifications

Technical Skills

• Strong proficiency in Python and experience with ML frameworks (PyTorch, TensorFlow, or JAX)
• Hands-on experience with LLMs, transformer architectures, and modern NLP techniques
• Solid understanding of software engineering principles, data structures, and algorithms
• Experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes)
• Familiarity with vector databases, embeddings, and semantic search technologies
• Proficiency with REST APIs, microservices architecture, and backend development
• Experience with version control (Git) and collaborative development workflows

Experience

• 3+ years of professional software engineering experience
• 2+ years working directly with machine learning or AI systems in production
• Demonstrated track record of shipping AI-powered products or features
• Experience with the full ML lifecycle: data preparation, training, evaluation, deployment, and monitoring

Soft Skills

• Strong problem-solving abilities and analytical thinking
• Excellent written and verbal communication skills
• Ability to work collaboratively in cross-functional teams
• Self-motivated with the ability to manage multiple priorities
• Pragmatic approach to balancing innovation with delivery
• Willingness to learn and adapt in a fast-paced environment

Preferred Qualifications

• Advanced degree (MS or PhD) in Computer Science, Machine Learning, or related field
• Experience with prompt engineering, RAG (Retrieval-Augmented Generation), and agent-based systems
• Familiarity with MLOps tools and practices (MLflow, Weights & Biases, Kubeflow)
• Knowledge of model compression, quantization, and optimization techniques
• Experience with A/B testing and experimentation frameworks for AI features
• Contributions to open-source AI/ML projects
• Experience with real-time inference and low-latency serving systems
• Understanding of AI safety, bias mitigation, and responsible AI practices
• Background in measurement, analytics, or data platform domains

What We Offer

• Competitive salary and equity compensation
• Comprehensive health, dental, and vision insurance
• Flexible work arrangements and remote-friendly culture
• Professional development budget for conferences, courses, and learning resources
• Access to cutting-edge AI tools and technologies
• Collaborative team environment with opportunities for growth and impact
• Opportunity to work on challenging problems at the intersection of AI and measurement
• Regular team events and knowledge-sharing sessions