Job Url: https://www.linkedin.com/jobs/search/?currentJobId=4326749134&distance=25.0&f_AL=true&f_TPR=r20000&f_WT=2&geoId=103644278&keywords=software%20engineer&origin=JOB_SEARCH_PAGE_JOB_FILTER&start=25 Job Description: Senior ML (Machine Learning) Engineer/Lead  United States · 2 hours ago · 29 applicants Promoted by hirer · No response insights available yet Remote Matches your job preferences, workplace type is Remote. Full-time Easy Apply Save Save Senior ML (Machine Learning) Engineer/Lead  at Infinite Computer Solutions Senior ML (Machine Learning) Engineer/Lead Infinite Computer Solutions · United States (Remote) Easy Apply Save Save Senior ML (Machine Learning) Engineer/Lead  at Infinite Computer Solutions Show more options Your profile is missing required qualifications Show match details Help me update my profile BETA Is this information helpful? Get personalized tips to stand out to hirers Find jobs where you’re a top applicant and tailor your resume with the help of AI. Try Premium for PKR0 People you can reach out to University of the Punjab logo School alumni from University of the Punjab Show all Meet the hiring team Deepak Kumar 3rd Manager- Talent Acquisition Job poster Message About the job About Infinite: About Infinite : Infinite is a global leader in digital engineering and IT services, with over 20 years of experience driving digital transformation. We partner with leading Fortune 1000 companies to deliver innovative, scalable technology solutions that accelerate business outcomes. With deep expertise in telecommunications, healthcare, banking, and finance, Infinite helps organizations optimize and modernize their technology landscapes to achieve long-term growth and efficiency. Overview: We are seeking a highly skilled Machine Learning Engineer to enhance and scale our existing ML pipeline and develop a robust annotation platform to streamline data labeling and model training workflows. This role is critical for improving efficiency, consistency, and adaptability across Walt Disney Imagineering. Key Responsibilities Pipeline Expansion & Optimization Analyze and extend the current ML pipeline to support new use cases, models, and data sources without disrupting existing workflows. Resource should have 10+ Years of experience Introduce modular enhancements for improved flexibility, maintainability, and performance. Implement parameterization and configuration options to make the pipeline adaptable for diverse projects. Optimize pipeline components for LLM interactions, including efficient data flow for prompt generation, fine-tuning, and inference. Ensure all updates are version-controlled, well-documented, and backward-compatible. Annotation Platform Development Architect and develop a custom annotation platform to support large-scale data labeling for supervised learning tasks. Implement features for role-based access, task assignment, and progress tracking. Integrate quality control mechanisms such as consensus checks, inter-annotator agreement, and automated validation. Enable scalable storage and retrieval of annotated datasets with versioning and audit trails. Provide APIs and integration points for seamless interaction with ML pipelines and data sources. Automation & Scalability Automate repetitive tasks such as data validation, model retraining, and performance monitoring. Optimize pipeline and annotation workflows for distributed processing and cloud scalability. Collaboration & Standards Work closely with Data Scientists, Data Engineers, and Product Managers to standardize ML development and annotation practices. Establish best practices for CI/CD in ML, including automated testing and deployment of models and annotation tools. Monitoring & Maintenance Implement robust monitoring systems for pipeline health, data drift, and annotation quality. Continuously improve pipeline and platform efficiency based on feedback and evolving business needs. Develop continuous evaluation pipelines for LLM-driven features using human and automated metrics. Required Skills Strong proficiency in Python, ML frameworks (TensorFlow, PyTorch). Experience with workflow orchestration tools (Temporal, Airflow, Prefect). Knowledge of containerization and cloud platforms (Docker, Kubernetes, AWS/GCP/Azure). Familiarity with data engineering principles and tools (SQL). Understanding of MLOps practices and annotation workflows. Experience with large language models (LLMs), including evaluation and integration in production pipelines. Preferred Qualifications Experience in enhancing existing ML pipelines and integrating new components. Experience in building annotation tools or integrating with existing platforms (e.g., SageMaker). Background in integrating multimodal models. Knowledge of model governance, prompt evaluation, and responsible AI principles for LLMs. Background in UI/UX design for data labeling interfaces. Strong problem-solving skills and ability to work in cross-functional teams. Annotation Platform Requirements, Architecture & Build vs. Buy Evaluation Conduct a market assessment of existing annotation tools (SageMaker, Encord, etc.) for feature fit, scalability, and cost. Compare against internal requirements for customization, integration, and security. Deliver a recommendation report outlining pros/cons, estimated effort, and ROI for build vs. buy. Document functional and technical requirements for annotation workflows, user roles, and quality control. Core Platform Features Implement annotation workflows: task assignment, progress tracking, dataset upload, etc. Enable data storage with versioning and audit trails. Develop APIs for integration with ML pipeline. Automation for Human-in-the-Loop Integrate pre-annotation capabilities using ML models to auto-label data before human review. Provide confidence scoring to prioritize human validation where model predictions are uncertain. Ensure seamless handoff between automated and manual steps for quality assurance. Scalability & Quality Control Add consensus checks, inter-annotator agreement, and automated validation. Build dashboards for reporting and bulk task management. Documentation & Training Deliver user guides, API documentation, and onboarding materials. ML Pipeline Enhancements (Including LLM Optimization) Pipeline Expansion Extend existing pipeline to support new data sources and preprocessing modules. Integrate annotation platform APIs for seamless data flow. LLM Integration & Optimization Incorporate LLM fine-tuning and inference steps into the pipeline. Develop reusable prompt templates and evaluation frameworks. Optimize latency, accuracy, and cost for LLM interactions. Implement monitoring for LLM performance metrics.