AI MLOps Engineer Jobs

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The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.

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Design, develop, and deploy AI/ML models ranging from traditional ML regression algorithms to transformer-based architectures. Train, fine-tune, and optimize deep learning and LLM-based solutions. Engage with customers to understand their needs and transform them into actionable tasks for developing new functionalities within the Articul8 platform. Collaborate with researchers, software engineers, and product teams to integrate new AI capabilities into applications. Implement and evaluate state-of-the-art automated testing and metrics to improve model accuracy and efficiency. Optimize models for both cloud and on-premises environments to ensure low latency and high availability. Develop APIs and micro-services to serve AI models in production. Stay current with the latest AI models, research, and best practices. Ensure ethical AI practices, data privacy, and security compliance.

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Lead, mentor, and grow a high-performing team of AI/ML engineers, fostering a culture of innovation, technical excellence, and continuous learning. Collaborate cross-functionally with Customer Success, Product Management, Engineering, and Business Development to scope, prioritize, and align AI/ML initiatives with core business objectives. Define and enforce best practices for the full ML lifecycle, including experimentation, code reviews, reproducibility, deployment pipelines, monitoring, and MLOps. Own the technical roadmap for AI/ML capabilities, ensuring alignment with long-term product strategy while rapidly adapting to research findings and market shifts. Drive translation of applied research into production-ready solutions, balancing cutting-edge innovation with pragmatic delivery at startup speed. Establish team processes for prioritization, planning, and technical guidance to optimize execution speed while ensuring reliability, scalability, and quality. Promote a data-driven culture by defining success metrics and KPIs, ensuring technical outputs are measurable, impactful, and tied to business outcomes. Contribute hands-on to technical architecture, model design, and code reviews where appropriate, while balancing technical leadership and management responsibilities. Advocate for responsible and ethical AI practices, ensuring compliance with organizational policies and industry standards.

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The Head of Machine Learning will manage 9 Machine Learning Engineers, including 3 Team Leaders, with responsibilities spanning People Management and project coordination. They will understand and coordinate the strategic direction of ML team projects, manage dependencies, allocate resources, and ensure alignment with business and product goals. This includes contributing to system architecture and development by empowering the team via 1:1s, code reviews, and discussions to deliver impactful features. The role involves leading and nurturing the ML engineering team through coaching and mentorship, leading team OKR discussions, coordinating projects, facilitating meetings, and collaborating with the CTO, Platform, and Product Managers to align team priorities with company OKRs. They will work with the People team on recruiting and onboarding talent, act as a sounding board for the team, support identifying and resolving bottlenecks and blockers to enable faster iteration, drive ML system development and deployment, optimize tools and infrastructure for efficiency, and promote a culture of collaboration and continuous learning while mentoring team members.

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The Founding AI Engineer is responsible for building end-to-end training pipelines covering data, training, evaluation, and inference. They design new model architectures or adapt open-source frontier models, fine-tune models using state-of-the-art methods such as LoRA/QLoRA, SFT, DPO, and distillation. They architect scalable inference systems using technologies like vLLM, TensorRT-LLM, and DeepSpeed, and build data systems for high-quality synthetic and real-world training data. The engineer develops alignment, safety, and guardrail strategies and designs evaluation frameworks across performance, robustness, safety, and bias. They own deployment responsibilities including GPU optimization, latency reduction, and scaling policies. Additionally, they shape early product direction, experiment with new use cases, and build AI-powered experiences from zero, exploring frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models. They take ownership of end-to-end problem solving, prototype, test, iterate, ship developments, and work with a mindset that values speed, discipline, humility, curiosity, and high standards within a founding team context.

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Build end-to-end training pipelines including data preparation, training, evaluation, and inference; design new model architectures or adapt existing open-source frontier models; fine-tune models using state-of-the-art methods such as LoRA/QLoRA, SFT, DPO, and distillation; architect scalable inference systems using technologies like vLLM, TensorRT-LLM, and DeepSpeed; build data systems for high-quality synthetic and real-world training data; develop alignment, safety, and guardrail strategies; design evaluation frameworks covering performance, robustness, safety, and bias; own deployment tasks focusing on GPU optimization, latency reduction, and scaling policies; shape early product direction, experiment with new use cases, and build AI-powered experiences from zero; explore frontier techniques including retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models.

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The Founding Machine Learning Engineer is responsible for shaping the core technical direction of the project including model selection, training strategy, infrastructure, and long-term architecture. The role involves building end-to-end training pipelines from data acquisition through training, evaluation, and inference. The engineer will design new model architectures or adapt existing open-source frontier models, fine-tune models using state-of-the-art methods, and architect scalable inference systems utilizing frameworks such as vLLM, TensorRT-LLM, and DeepSpeed. Responsibilities also include building data systems for producing high-quality synthetic and real-world training data, developing alignment, safety, and guardrail strategies, and designing evaluation frameworks that assess performance, robustness, safety, and bias. The engineer will own deployment processes focusing on GPU optimization, latency reduction, and scaling policies. Additionally, the role involves shaping early product direction, experimenting with new use cases, and building AI-powered experiences from scratch, exploring frontier techniques such as retrieval-augmented training, mixture-of-experts, distillation, multi-agent orchestration, and multimodal models.

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The VP of Engineering is responsible for building, scaling, and upholding the technical backbone of a global AI product through direct hands-on development with real systems and code. The role involves personally building and maintaining core AI infrastructure, designing model training, evaluation, and deployment pipelines, debugging and resolving production AI failures, reviewing and merging critical pull requests, defining standards for model lifecycle and experimentation, designing the organizational structure and hiring strategy, and aligning the AI roadmap with business goals. This position requires leading the engineering culture and execution by example rather than purely managing others, and acting as the final technical decision-maker to ensure AI quality, reliability, and scalability end-to-end while balancing research ambition with real product delivery.

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The responsibilities include developing AI models and integrating Agentic AI for routing, dispatching, and prediction, specifically using features extracted from knowledge graphs to develop AI-based optimal routing, dispatching technologies, demand prediction, ETA prediction, and improving analytic prediction models. The role also involves designing and implementing the integration architecture with Agentic AI systems. Additionally, responsibilities cover the design and development of mobility and logistics-specific ontologies, building knowledge graph-based data models, integrating and refining large heterogeneous data, and managing relationships among service entities to enhance data intelligence. Furthermore, the position requires designing, building, and operating large-scale data pipelines (ETL/ELT) for UMOS platforms, establishing and automating MLOps pipelines for stable model operation, and developing and integrating efficient API interfaces with service backend systems.

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Design and ship advanced ML systems, especially LLM-driven agents and self-improving workflows. Build robust data and training pipelines, enable fast experimentation, and ensure models and agents continuously improve in production. Build LLM-based agents, tool-using workflows, and autonomous self-improvement loops. Design, train, and evaluate ML models across NLP/LLM, vision, and retrieval domains. Develop data pipelines, training code, experiment tooling, and automated deployment systems. Use PyTorch for model development and W&B (or similar) for tracking experiments and lineage. Implement monitoring for performance, drift, safety, and agent behavior. Optimize inference for latency, throughput, and cost. Work closely with engineering and product teams to turn prototypes into reliable production features. Establish ML engineering best practices and mentor teammates.