AI Engineer

• Build and maintain data pipelines on Azure, Databricks, and Microsoft Fabric
• Design and support analytical data models for reporting, dashboards, and AI use cases
• Develop Python scripts for data ingestion, transformation, automation, and analysis
• Support AI/ML solutions (incl. RAG use cases) and assist in model deployment
• Create and maintain Power BI datasets and reports
• Partner with business teams to translate requirements into data/AI solutions
• Ensure data quality, testing, documentation, and adherence to best practices
• Follow data governance, security, and compliance standards

Data Engineer, AI

Machine Learning Engineer

• Architect the ranking backbone for query understanding and personalization
• Design and build a search autosuggest system
• Develop production-grade Multi-Task Learning
• Own the inference layer for quality optimization
• Advance evaluation practices for long-horizon metrics
• Partner across teams to translate business goals into ranking policies
• Mentor ML engineers in ranking expertise

Senior Machine Learning Engineer II, Search & Recommendations Ranking

• Design and implement scalable APIs for agent orchestration and learner interaction
• Build data pipelines that feed agent evaluation and continuous improvement cycles
• Create event streaming infrastructure for real-time learner interaction analysis
• Ensure data quality and accessibility for training and evaluating learning agents
• Improve data infrastructure for cost and performance in close coordination with Data and ML teams
• Design and implement rigorous evaluation frameworks to measure agent performance and improve cycles
• Develop LLM evaluation processes and perform error analysis to identify systemic improvements for agentic learning systems
• Support instrumentation that makes evaluation relevant (metrics, traces/logs, and analysis workflows)
• Participate in on-call rotation and incident response for learning platform reliability
• Contribute to runbooks, postmortems, and reliability improvements as we evolve our operating model

Senior Software AI Engineer

• Develop comprehensive systems and frameworks for AI applications and products.
• Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
• Contribute to the creation of robust data pipelines for model evolution and production systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.
• Actively participate in a team that exercises principled, agile-like development practices.
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work, as well as conduct peer reviews over the work of others as requested.
• Guide junior AI engineers on AI/ML and industry best practices and methodologies.
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Support and participate in meetings with external stakeholders.

AI Engineer – III

• Design, develop, and deploy scalable AI solutions
• Build LLM-based solutions for enterprise applications
• Integrate LLMs with business systems
• Architect AI agents for intelligent automation
• Implement CI/CD pipelines for machine learning models
• Monitor model performance and governance
• Collaborate with cross-functional teams for solution delivery
• Create technical documentation and mentoring

• Design, build, and train scalable machine learning models and algorithms for cloud-based environments
• Collect, preprocess, and analyze large and complex datasets to support model development and continuous refinement
• Transition models from research to production, ensuring they are performant, maintainable, and ready to operate at scale
• Deploy ML models into existing or newly developed backend services, integrating seamlessly with cloud infrastructure
• Partner closely with engineers and product managers to deliver ML solutions that enhance and support our connected devices
• Test, evaluate, and continuously monitor deployed models to ensure accuracy, robustness, and early detection of data drift or performance degradation
• Build strong collaborative relationships with technical subject matter experts, stakeholders, and leadership across the organization
• Identify, communicate, and manage technical risks throughout the project lifecycle to ensure smooth delivery

Machine Learning Engineer – Cloud

• Develop and optimize AI/ML models for resource-constrained edge devices
• Collect, preprocess, and analyze datasets to train and validate models
• Apply model compression, quantization, and pruning techniques
• Ensure models perform reliably in real-world environments
• Collaborate closely with hardware engineers on AI model integration
• Identify and manage technical risks throughout the project

Machine Learning Engineer – Edge

• Work on high-impact problems at the intersection of LLM research, large-scale ML systems, and real-world e-commerce applications.
• Choose to work in areas like query understanding, search relevance and ranking, generative recommendations, LLM evaluation, and more.

Machine Learning Engineer, PhD Intern

• Position SummaryThe Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data preprocessing, model training, and fine-tuning using large-scale healthcare datasets. This role requires a strong understanding of Large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data.
• Key Responsibilities
• - Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling missing values, outlier detection, feature engineering, and data normalization. Identify, collect, and curate relevant, industry-specific datasets for model retraining. Format data appropriately for the chosen LLM and training pipeline
• - Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software. Train and fine-tune pre-trained LLMs on the custom dataset to achieve specific goals. Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance. Integration of structured + unstructured data (multi-modal/multi-input models)
• - Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.
• - Pipeline Development: Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.
• - Collaboration: Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.
• - Research & Development: Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions.
• - Documentation: Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.

Senior Machine Learning Engineer

• Play a key role in building the internal platform which supports training and deploying AI models across the entire organization.
• Take ownership of defining the platform to enable AI model fine-tuning and batch inference by building the SDKs and supporting the infra.
• Manage cross-cutting stakeholder relationships, prioritizing customer needs first.
• Navigate ambiguous, hairy, and technical problem spaces.
• Jump into domains, languages, and problem areas that might be new and unfamiliar.

Senior Software Engineer II, ML/AI Platform

• Own full sales cycle from prospecting through close across enterprise and strategic accounts, driving net-new business and expansion
• Develop and execute strategic account plans that drive long-term growth, including multi-threading across technical, business, and executive stakeholders
• Lead complex, value-based sales engagements, positioning Arrow’s cloud marketplace (ArrowSphere) and AI solutions (AI 360) within broader customer transformation initiatives
• Deliver executive-level presentations, workshops, and solution demos, translating technical capabilities into clear business outcomes
• Build and maintain a robust, accurate pipeline, with strong discipline around forecasting, deal progression, and CRM hygiene
• Identify, qualify, and advance opportunities by aligning to customer priorities, architecture, and business strategy
• Drive “land and expand” motions, growing footprint within accounts by going wide (cross-functional) and high (executive level)
• Partner cross-functionally with sales engineering, product, marketing, and leadership to design and deliver tailored solutions
• Contribute to go-to-market strategy, providing field feedback to shape product roadmap, partner strategy, and platform evolution
• Engage with partners (MSPs, CSPs, distributors, and ecosystem players) to accelerate pipeline and drive joint opportunities
• Build compelling ROI-driven business cases and lead commercial negotiations through to close

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