Role Description This is a remote position. Shuru is a self-managed technology team specializing in accelerating visions through product, technology, and AI leadership. With a focus on bespoke execution, we deliver impactful solutions that are scalable and designed for success. At Shuru, we deliver mobile solutions that meet and exceed customer expectations. Our collaborative and fast-paced environment encourages creativity and innovation. We are hiring an MLOps Engineer for a leading workforce solutions company that is undergoing a major technology-led transformation. A key part of this transformation is the deployment of robust analytics and machine learning products that combine data, business knowledge, and enterprise goals to drive measurable business impact. The MLOps Engineer will play a critical role in building, deploying, and maintaining enterprise machine learning solutions. This role will focus on: - Designing scalable ML pipelines - Managing cloud-based ML environments - Supporting model governance - Ensuring high standards of model performance, reliability, and operational excellence You will work closely with data science, product, architecture, engineering, and business teams to bring machine learning solutions from development to production and support their continued improvement. As an MLOps Engineer, you will: - Work with stakeholders to define machine learning solution designs based on cloud services such as Azure and Snowflake. - Design, build, and maintain machine learning pipelines and frameworks to support enterprise analytics, reporting, and product needs. - Collaborate with data science, ML engineering, data quality, and product teams on model deployment architecture and implementation. - Manage, configure, and optimize cloud environments and related machine learning services. - Implement tools and processes for model integration, storage, profiling, monitoring, processing, management, and archival. - Support enterprise-wide model governance, performance tracking, and lifecycle management standards. - Recommend improvements to ML platforms, tools, and development practices to support strategic technology and business objectives. - Work with SaaS vendors and strategic partners to implement and maintain modern machine learning solutions. - Use Agile practices to manage delivery, contribute to project planning, and support successful rollout of ML products. - Partner with internal stakeholders to understand business requirements and translate them into reliable ML operations solutions. - Stay current with emerging MLOps tools, cloud technologies, and best practices to ensure solutions remain scalable, secure, and fit for purpose. Qualifications - Bachelor's degree in Computer Science, Engineering or Technical Field preferred. - Minimum 3-7 years of relevant experience. - Proven experience in machine learning engineering and operations. - Profound understanding of machine learning concepts, model lifecycle management, and experience in model management capabilities including model definitions, performance management and integration. - Execution of model deployment, monitoring, profiling, governance and analysis initiatives. - Excellent interpersonal, oral, and written communication; Ability to relate ideas and concepts to others; write reports, business correspondence, project plans and procedure documents. - Solid Python, ML frameworks (e.g., TensorFlow, PyTorch), data modeling, and programming skills. - Experience and strong understanding of cloud architecture and design (AWS, Azure, GCP). - Experience using modern approaches to automating machine learning pipelines. - Agile and Waterfall methodologies. - Ability to work independently and manage multiple task assignments within a structured implementation methodology. - Personally invested in continuous improvement and innovation. - Motivated, self-directed individual that works well with minimal supervision. - Must have experience working across multiple teams/technologies. - Preferred but not essential: Experience with business intelligence tools (preferably PowerBI). - Preferred but not essential: Experience with MLOps tools (e.g., MLflow, Kubeflow). Benefits - Work on global projects with clients from worldwide. - Be part of a remote-first culture - work from anywhere with flexibility. - Enjoy team-building activities and regular outings. - Collaborate and grow in a supportive environment with opportunities to learn from senior engineers. - Competitive salary and benefits package.

• Design, develop, and implement AI/ML solutions to solve business problems, contributing to projects from proof of concept and pilot through production deployment. • Build and apply both traditional machine learning models and modern AI techniques, including large language models (LLMs), generative AI, retrieval-augmented generation (RAG), and agentic AI systems. • Collaborate with senior engineers, product managers, data scientists, and business stakeholders to refine project scope, technical approach, success metrics, and deployment requirements. • Contribute to building scalable and maintainable AI systems with a focus on accuracy, robustness, fairness, and explainability, adhering to responsible AI principles. • Apply model evaluation frameworks to assess performance, generalization, bias, and resilience to edge cases or adversarial inputs. • Support the transition of AI prototypes into production-ready systems by partnering with platform and engineering teams on deployment, monitoring, and lifecycle management. • Contribute to documentation, governance, and compliance processes to meet enterprise and regulatory standards, especially in healthcare and other regulated environments.

• Drive the operational excellence of our AI/ML Platform • Design and implement automated deployment pipelines • Collaborate with teams to maintain scalable infrastructure • Develop and maintain monitoring and logging systems • Work closely with data engineers for efficient data pipelines • Implement version control systems for machine learning models • Contribute to the governance of machine learning models • Enforce security best practices in MLOps • Identify opportunities for process automation • Contribute to incident response and system recovery

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks • Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives • Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy • Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context • Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps • Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment • Drive technical execution with high autonomy, making critical design and implementation decisions independently