Note: The job is a remote job and is open to candidates in USA. SoTalent seeks a Principal AI and Machine Learning Engineer to lead the vision, architecture, and execution of enterprise-scale AI and multimodal data mining platforms. The role focuses on large-scale machine learning systems, multimodal and generative AI, distributed infrastructure, and technical leadership across teams.
Responsibilities
- Define and execute the long-term machine learning and AI roadmap for large-scale multimodal data mining platforms
- Translate complex and ambiguous business challenges into measurable technical objectives and engineering strategies
- Balance foundational platform investments with short-term delivery priorities to maximize business impact
- Drive architectural decisions that support scalability, efficiency, reliability, and future innovation
- Establish technical standards, best practices, and governance for machine learning development and deployment
- Design, develop, and optimize enterprise-scale machine learning systems supporting multimodal data processing and analysis
- Lead development of distributed training and inference solutions for large-scale models
- Architect platforms capable of processing and mining massive datasets across cloud-based environments
- Build systems that efficiently scale model training, evaluation, retrieval, and deployment workflows
- Continuously improve model performance, efficiency, and operational reliability
- Develop and deploy multimodal machine learning solutions involving text, image, video, sensor, and structured data
- Design architectures leveraging foundation models, transformer-based models, embeddings, and advanced representation learning techniques
- Lead initiatives involving model pretraining, fine-tuning, alignment, and adaptation strategies
- Drive innovation in multimodal retrieval, semantic understanding, knowledge discovery, and intelligent search systems
- Evaluate emerging AI methods and integrate advancements into production environments
- Build intelligent data pipelines that support large-scale data discovery, curation, and model improvement workflows
- Develop automated feedback loops that continuously improve training datasets and model performance
- Implement active learning, teacher-student architectures, and automated labeling frameworks
- Design systems that accelerate error analysis, model evaluation, and dataset refinement processes
- Optimize data acquisition, processing, and lifecycle management at enterprise scale
- Develop AI-powered systems utilizing Large Language Models (LLMs), reasoning frameworks, and agent-driven workflows
- Design intelligent automation pipelines that improve decision-making, data analysis, and operational efficiency
- Implement retrieval-augmented generation (RAG), tool-use workflows, and advanced reasoning architectures
- Explore reinforcement learning approaches for problem-solving and decision optimization
- Build scalable AI systems capable of supporting evolving business and technology needs
- Lead design and optimization of distributed machine learning infrastructure
- Manage large-scale training workloads across GPU clusters and cloud environments
- Optimize training efficiency, inference latency, resource utilization, and operational costs
- Develop scalable deployment and serving strategies for enterprise AI applications
- Drive improvements in performance, reliability, monitoring, and production readiness
- Partner with product, engineering, infrastructure, data science, and business teams to align AI initiatives with organizational priorities
- Act as a technical bridge across teams, ensuring successful integration of new capabilities into production systems
- Influence product strategy through technical expertise and market awareness
- Coordinate large-scale technical initiatives spanning multiple organizations and stakeholders
- Communicate complex AI concepts clearly to technical and executive audiences
- Serve as a hands-on technical leader while mentoring engineers and machine learning practitioners
- Guide teams through architecture decisions, implementation strategies, and technical trade-offs
- Foster engineering excellence through coaching, knowledge sharing, and best practice development
- Help build a culture of innovation, accountability, collaboration, and continuous learning
- Lead by example through active contribution to critical technical initiatives
Skills
- Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical field, or equivalent professional experience
- 12+ years of experience developing and deploying machine learning systems in production environments
- Proven experience leading end-to-end machine learning platforms from research through deployment and operational support
- Extensive experience defining technical roadmaps and driving large-scale AI initiatives
- Demonstrated success solving complex technical challenges involving machine learning, distributed systems, and production infrastructure
- Experience leading cross-functional programs and influencing technical strategy across organizations
- Deep expertise in machine learning, deep learning, and large-scale AI systems
- Experience training foundation models and large-scale neural networks
- Strong understanding of transformers, representation learning, multimodal AI, and advanced deep learning architectures
- Experience with model evaluation, optimization, and production deployment
- LLM architecture, fine-tuning, and inference optimization
- Retrieval-Augmented Generation (RAG)
- Agentic AI and tool-use frameworks
- Reinforcement Learning and alignment techniques
- Prompt engineering and reasoning systems
- Knowledge distillation and model compression
- Distributed training and inference architectures
- Large-scale data pipeline development
- Data curation and annotation workflows
- Streaming and batch processing systems
- High-performance computing and GPU optimization
- Large-scale retrieval and search infrastructure
- AWS, Azure, or Google Cloud Platform
- MLOps frameworks and model lifecycle management
- CI/CD for machine learning systems
- Monitoring, observability, and performance optimization
- Containerization and orchestration technologies
- Scalable model serving and inference platforms
- Master's or PhD in Computer Science, Machine Learning, Artificial Intelligence, Robotics, or a related discipline
- Experience in autonomous systems, robotics, or real-time decision-making environments
- Experience with multimodal foundation models and embodied AI systems
- Background in large-scale retrieval, search, and recommendation systems
- Experience designing agent-based reasoning frameworks and AI-powered workflow automation
- Publications, patents, conference presentations, or significant open-source contributions
Benefits
- Remote work (US)
Company Overview