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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Artificial Intelligence & Machine Learning Engineer, Vice President - AI Labs - **Company:** Blackrock, Inc. - **Location:** Atlanta, GA, United States (Remote available) - **Experience:** Expert - **Salary:** $170,000.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Computer-Aided Design, Artificial Intelligence, Amazon Web Services, Computer Vision, Big Data, C++ (Programming Language), Cloud Computing, Database Queries, Software Debugging, Distributed Systems, R (Programming Language), Python (Programming Language), Machine Learning, Natural Language Processing, Performance Tuning, Tensorflow, Azure Machine Learning, Software Deployment, Software Engineering, Model-Driven Development, Pytorch, Large Language Models, Deep Learning, Generative AI, Scikit Learn, Machine Learning Operations, Api Design, Data Pipelines, Docker, Programming Languages - **Published:** September 21, 2026 - **Apply:** https://find.jobs/jobs-near-me/apply/ats-redirect/?id=2964205833-2 ## About the Role * 12+ years of software engineering experience, including significant experience designing, building, and operating complex production systems. * 6+ years of experience building and deploying AI/ML, optimization, or other model-driven applications in production. * Strong proficiency in Python and a track record of developing maintainable, well-tested production software. * Strong SQL skills and experience working with data-intensive applications and large-scale datasets. * Experience designing and building APIs, services, data pipelines, or distributed systems that support AI/ML applications. * Experience with containerization and orchestration technologies, such as Docker and Kubernetes. * Demonstrated ability to lead complex technical solutions from architecture and design through production deployment, operation, and continuous improvement. * Strong problem-solving, collaboration, and communication skills, with the ability to influence technical decisions across multidisciplinary teams. Additional knowledge and experience we value includes: * Experience collaborating with research teams in fast-moving environments, ideating quickly on research prototypes. * Generative AI, large language models, AI agents, and architectures for production AI systems. * Evaluation, experimentation, observability, debugging, and testing approaches for AI/ML systems, including non-deterministic and agentic applications. * Machine learning and optimization frameworks such as PyTorch, TensorFlow, or JAX. * Distributed systems, cloud infrastructure, model serving, orchestration, data pipelines, and API design. * Performance optimization across latency, throughput, compute utilization, reliability, scalability, and cost. * Responsible AI, security, governance, and controls in enterprise or regulated environments. * Experience with a systems- or performance-oriented programming language such as Rust, C++, C, Go, or Zig. * Experience in financial services or another regulated industry. Relevant Knowledge, Skills, and Abilities: * Machine learning frameworks (TensorFlow, PyTorch, scikit-learn) * Programming languages (Python, R, Java) * Deep learning, NLP, computer vision techniques * Cloud ML platforms (AWS SageMaker, Azure ML, Vertex AI) * MLOps tools and practices * Statistical analysis and experimentation ## Description * Provide technical leadership, set architectural direction, and drive key design decisions across multidisciplinary teams. * Design, build, and operate production AI/ML systems across areas such as generative AI, intelligent agents, predictive modeling, and optimization. * Develop reusable AI platforms, services, and infrastructure that accelerate experimentation, deployment, monitoring, and adoption across the firm. * Translate emerging AI capabilities into robust, scalable solutions that deliver measurable business impact. * Define evaluation, testing, and observability approaches for AI systems, including non-deterministic and agentic applications. * Optimize AI systems and workloads for latency, throughput, compute efficiency, reliability, security, and cost. * Partner with business, engineering, data, risk, and control teams to take solutions from concept through production deployment and ongoing operation. * Establish engineering standards, mentor engineers, and influence technical strategy across teams. ## Related Videos - [Alibaba Big Data and Machine Learning Technology](https://www.wearedevelopers.com/videos/37-alibaba-big-data-and-machine-learning-technology) - [Agentic employees in world's most downloaded FinTech app](https://www.wearedevelopers.com/videos/100123-agentic-employees-in-world-s-most-downloaded-fintech-app) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. 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