• Participate in data discovery workshops to inventory source systems including property management platforms, marketing channels, and CRM data, and translate findings into data lake architecture requirements. • Design and implement a multi-zone enterprise data lake on Amazon S3 (raw, conformed, enriched, aggregated) with ingest, cleansing, and business layers aligned to the SOW architecture. • Build batch and streaming data ingestion pipelines using AWS Glue, Amazon Kinesis, and AWS Data Pipeline across CDP, marketing, and property management data sources. • Implement data transformation and orchestration frameworks using AWS Glue ETL and AWS Step Functions, including AWS Glue Data Catalog for metadata management and discovery. • Configure Amazon Athena for serverless SQL querying across the data lake; support QuickSight integration with curated data sets for business analytics. • Develop and deploy ML models on Amazon SageMaker for lead scoring, predictive maintenance, intelligent underwriting risk scoring, and AI-powered audience segmentation. • Integrate Amazon Bedrock foundation models to enable generative AI capabilities including customer profile enrichment, hyper-personalization, and intelligent marketing automation. • Use Kiro CLI to accelerate AI-assisted development workflows, spec-driven pipeline implementation, and automated code generation tasks. • Design and implement entity resolution pipelines using Amazon Entity Resolution to identify, deduplicate, and merge customer records into unified golden records. • Implement real-time and batch data synchronization pipelines between source systems and the Customer Data Platform (CDP). • Support Azure data lake migration: conduct discovery, assess schemas and transformation logic, provision AWS target environments, execute migration via AWS DataSync, and perform data validation and reconciliation. • Implement data lake security using AWS Lake Formation, including row-level security and column-level encryption. • Build and maintain data models to support Customer 360 views, ML feature stores, and executive analytics dashboards. • Ensure data quality, validation, and integrity across all pipeline stages and ML model outputs; support UAT for data-dependent features. • Collaborate with Full Stack, DevOps/MLOps, and AWS engagement teams; contribute to architecture documentation, pipeline runbooks, and data governance documentation.

Role Description Senior AI/ML Engineers are senior individual contributors who design, build, and deploy production-grade AI/ML systems for both client-facing and internal products. They partner with leadership and cross-functional teams to translate business needs into scalable ML and LLM-based solutions. This role does not typically include direct reports but requires strong technical leadership, mentorship, and influence across teams. Responsibilities - AI/ML System Design & Leadership - Lead the design and implementation of scalable ML systems, including supervised, unsupervised, and LLM-based solutions. - Translate research and prototypes into production-ready systems. - Partner with stakeholders to identify high-impact AI/ML opportunities and define optimal technical approaches. - Provide technical mentorship and contribute to team upskilling. - LLM & Production AI Systems - Build and operate LLM pipelines, including prompt design, fine-tuning, and evaluation. - Develop RAG-based systems using embeddings, vector stores, and retrieval strategies. - Design evaluation frameworks, feedback loops, and datasets to continuously improve model performance. - Create reusable tooling to accelerate experimentation, deployment, and monitoring. - MLOps & Deployment - Own end-to-end ML lifecycle: data pipelines, training, deployment, monitoring, and iteration. - Establish best practices for reproducibility, observability, CI/CD, and model versioning. - Partner with platform/DevOps teams to ensure reliability and scalability. - Promote responsible AI practices, including governance, fairness, and transparency. - Cross-Functional Collaboration - Lead cross-functional initiatives across data engineering, analytics, and AI/ML. - Translate complex ML concepts into clear recommendations for technical and non-technical audiences. - Collaborate with clients and internal teams to plan and deliver AI/ML solutions. - Contribute documentation, frameworks, and shared best practices. - Project Execution - Scope and lead complex AI/ML initiatives aligned to business outcomes. - Align stakeholders and drive execution across teams. - Establish clear success metrics and ensure delivery of high-impact solutions. Qualifications - 5–7 years of experience in ML engineering, AI engineering, or related fields, with production deployment experience. - Strong programming skills in Python and SQL; experience with PyTorch and HuggingFace. - Experience building LLM applications, including RAG, embeddings, and vector search. - Experience with cloud platforms (AWS or Azure; e.g., SageMaker, Bedrock, Azure ML). - Strong understanding of ML fundamentals: data design, training, evaluation, and experimentation. - Familiarity with LLM alignment techniques (e.g., SFT, DPO, RL). - Experience with MLOps practices: CI/CD, monitoring, retraining, and experiment tracking. - Proficiency working with complex, multi-source datasets and defining evaluation strategies. - Strong software engineering fundamentals (testing, modularity, code review). - Experience mentoring engineers and influencing technical direction. - Strong communication skills with both technical and non-technical stakeholders. Tech Stack - Languages: Python, SQL - Frameworks: PyTorch, HuggingFace - Platforms: AWS, Azure, Snowflake, Databricks Physical Requirements - Frequent sitting at a desk performing work on a computer. - Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. Compensation Compensation Range: $175,000 - 190,000 annually. Please note that compensation packages are finalized after the interview process is concluded. We use a competency-based approach to base pay, which means it is based on the competencies and skills demonstrated for this role. Core Company Values - Take the Long View - Ensure the company is built to last. - Be Courageous - Make the right decisions even when they aren't the easiest decisions. - Be Genuine - Bring honesty and authenticity to all that you do. - Work with Focus + Passion - Display purpose and pride in your work and never stop learning. As an equal opportunity employer, we are firmly committing to diversity, equity, and inclusion in our hiring efforts. We recognize that we need team members from all backgrounds and experiences to successfully shape a positive employee experience as well as deliver our product and service solutions. To that end, we actively seek candidates who can bring diverse experiences and backgrounds to our team. We know that complex factors and systemic bias can get in the way of us meeting strong candidates, so please don't hesitate to apply even if you're not 100% sure. At this time, Velir does not sponsor candidates and unfortunately cannot accept those on OPT or CPT.

• Build and experiment with ML models and LLM-powered systems (classification, embeddings, retrieval, agents). • Work on real-world use cases such as retail personalization, workflow automation, and customer intelligence. • Design and improve prompt pipelines, evaluation frameworks, and agent behaviors. • Collaborate on data pipelines: ingestion, cleaning, feature engineering, and analysis. • Prototype and ship features quickly in a production environment. • Contribute to internal tooling for model monitoring, evaluation, and iteration.

Role Description Join Avalara's AI and Machine Learning team and help build intelligent systems that improve how businesses manage tax compliance and automation. You will design and deliver production-grade AI and ML solutions that power document intelligence, classification, retrieval, and GenAI-driven workflows across Avalara's products and platforms. You will work with engineers, product managers, infrastructure teams, and domain experts to turn complex challenges into scalable, reliable solutions that create measurable customer and growth. As a senior individual contributor, you will help raise the engineering bar through technical leadership, operational excellence, and mentorship. You will report to Sr Manager, ML Engineering with a #LI-Remote work arrangement. Responsibilities - Design, build, and improve production-grade machine learning systems for classification, document understanding, retrieval, and AI-powered automation. - Fine-tune, evaluate, and deploy transformer-based models, small language models, and other machine learning approaches to solve business problems. - Build and optimise real-time inference services, batch processing pipelines, APIs, and model-serving workflows. - Develop evaluation frameworks that measure model quality, reliability, latency, cost efficiency, and operational performance. - Contribute to GenAI platform capabilities including retrieval-augmented generation, embeddings, prompt orchestration, document ingestion, and agent-based workflows. - Deliver secure, scalable, and observable ML and AI services with operational ownership. - Partner with product, engineering, infrastructure, and business stakeholders to define and implement practical AI solutions. - Improve engineering quality through testing, monitoring, documentation, and continuous improvement practices. - Mentor engineers and share knowledge, frameworks, and reusable patterns that strengthen AI capabilities across the organisation. - Drive technical decisions by balancing accuracy, latency, scalability, maintainability, security, and cost. Qualifications - Bachelor's degree in Computer Science, Engineering, or a related technical field, with 5+ years of experience building and operating production machine learning or AI systems. - Strong software engineering experience in Python, including building backend services, APIs, or distributed systems. - Hands-on experience developing, evaluating, deploying, and maintaining machine learning models in production environments. - Experience with modern AI techniques such as transformers, embeddings, retrieval-augmented generation, large language models, small language models, classification systems, and document intelligence solutions. - Use data, experimentation, and production insights to improve measurable outcomes such as automation quality, reliability, latency, cost efficiency, and developer productivity. Benefits - Total Rewards: In addition to a great compensation package, paid time off, and paid parental leave, many Avalara employees are eligible for bonuses. - Health & Wellness: Benefits vary by location but generally include private medical, life, and disability insurance. - Inclusive culture and diversity: Avalara strongly supports diversity, equity, and inclusion, and is committed to integrating them into our business practices and our organizational culture. Company Description We’re defining the relationship between tax and tech. We’ve already built an industry-leading cloud compliance platform, processing over 54 billion customer API calls and over 6.6 million tax returns a year. Our growth is real - we're a billion dollar business - and we’re not slowing down until we’ve achieved our mission - to be part of every transaction in the world. We’re bright, innovative, and disruptive, like the orange we love to wear. It captures our quirky spirit and optimistic mindset. It shows off the culture we’ve designed, that empowers our people to win. We’ve been different from day one. Join us, and your career will be too. We’re An Equal Opportunity Employer: Supporting diversity and inclusion is a cornerstone of our company — we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know.