• Work closely with the Head of Sales Engineering to define and execute the technical sales strategy for our VOC product line • Partner with the VOC Product Go-To-Market (GTM) team to translate product capabilities into compelling, market-facing value propositions and sales tools • Act as the VOC industry authority for the Field Sales and Sales Engineering teams, providing strategic guidance on market trends, competitive landscape, and customer pain points • Develop and scale Field Expertise within the Sales Engineering team, focusing on general VOC industry knowledge and in-depth technical understanding of our VOC solutions • Design and deliver comprehensive training programs, workshops, and resources to improve the SE team's ability to articulate value, handle technical objections, and close deals • Create and maintain a repository of high-quality sales assets, including a master deck, competitive positioning documents, and best-practice guides • Develop cutting-edge product demonstrations and Proofs of Concept (POCs) that clearly illustrate the unique value and technical superiority of our VOC solutions for target personas and industries • Engage directly in key, complex, or strategic sales cycles as the senior-level VOC expert, providing technical validation and building confidence with executive and technical buyers • Contribute to the development of winning sales strategies, including playbook creation and effective integration of VOC solutions into broader enterprise sales motions

• Lead technical discovery sessions with prospective clients • Understand client business problems and translate them into ML solutions • Design end-to-end ML architectures and technical proposals • Create compelling technical presentations and demonstrations • Estimate project scope, timelines, cost, and resource requirements • Support General Managers in winning new business • Serve as the primary technical point of contact for clients • Manage technical stakeholder expectations • Present technical solutions to both technical and non-technical audiences • Navigate complex organizational dynamics and conflicting priorities • Ensure client satisfaction throughout the project lifecycle • Build long-term trusted advisor relationships • Collaborate with delivery teams to ensure smooth handoff • Provide technical guidance during project execution • Contribute to the development of reusable solution patterns • Share learnings and best practices with ML practice • Mentor engineers on client communication and solution design

• Conduct deep technical discovery and align AI/data initiatives with business objectives • Architect scalable solutions across on-prem, cloud, and hybrid environments • Deliver customized demos, workshops, and executive briefings • Lead end-to-end POV deployments • Troubleshoot performance, integration, and security issues during rollout • Drive production adoption through documentation, enablement, and expansion planning • Serve as the technical liaison between customers and internal Product/Engineering teams

Role Description We are looking for a Sr. Machine Learning Architect to join our Machine Learning team. In this role, you will lead the architecture and implementation of production-grade machine learning and data solutions that enable customers to realize tangible business value from their data. You will collaborate closely with clients, data scientists, data engineers, platform/DevOps teams, and practice leadership to deliver high-quality solutions and advance phData’s delivery excellence. Key Responsibilities - Own and drive end-to-end architecture, solution design, and delivery of machine learning and data solutions for enterprise clients across diverse industries. - Translate business and data science requirements into scalable technical and MLOps solutions that align with phData methodologies, standards, and best practices. - Ensure engagements are delivered on time, within scope, and with measurable business value for clients. - Design and create secure, scalable environments and tooling for data scientists to build, train, and manipulate models and data. - Work within customer technology ecosystems to extract data from a variety of source systems and place it within analytical and model-training environments. - Define deployment approaches and production infrastructure for machine learning models, ensuring that businesses can reliably use, monitor, and maintain the models we develop. - Demonstrate and reveal the business value of data by partnering with data scientists to manipulate and transform data into actionable insights and deployable machine learning models. - Create and execute operational testing strategies, including QA validation, performance testing, and implementation plans, to support model testing and deployment. - Ensure the quality, reliability, and observability of delivered solutions through testing, documentation, logging, and monitoring. Collaboration & Leadership - Collaborate with cross-functional partners, including data science, data engineering, platform/DevOps, and business stakeholders, to deliver successful client engagements. - Provide technical and strategic leadership during workshops, discovery sessions, architecture and design reviews, and project delivery. - Ensure high quality in deliverables through code reviews, documentation, testing, governance, and adherence to security and compliance standards. - Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery, and standardize patterns for deploying and operating ML solutions. - Serve as a technical thought leader for clients, recommending technologies and solution designs for model inference, retraining, monitoring, and lifecycle management from the application layer down to infrastructure. Practice & Firm Contribution - Contribute to internal initiatives such as IP development, accelerators, reference architectures, templates, playbooks, and training related to machine learning engineering and MLOps. - Represent phData with professionalism in all interactions, communicating clearly with both technical and non-technical stakeholders. Additional Responsibilities - Act as a trusted advisor to senior client stakeholders, shaping roadmaps, influencing strategic decisions, and guiding long-term initiatives. - Mentor and coach team members, fostering a culture of learning, feedback, and continuous improvement. - Help define and refine practice standards, reusable assets, and delivery frameworks. Qualifications - 8+ years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer building and deploying production data and machine learning solutions. Requirements - Hands-on expertise in modern programming languages such as Python, Scala, Java, or similar, including experience developing APIs and web applications using frameworks such as Flask, Django, or Spring. - Experience building and operating robust data pipelines and distributed data processing solutions using SQL and big data technologies (e.g., Spark, Snowflake, Databricks, Redshift, Amazon EMR, HDFS). - Strong systems-level knowledge of network and cloud architecture, Linux-based operating systems, and data/storage platforms (e.g., AWS, Databricks, Cloudera), with familiarity across data and messaging systems such as JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, and SAP; proven experience deploying machine learning models in production environments. - Strong working knowledge of SQL and the ability to write, debug, and optimize complex and distributed queries. - Hands-on experience with one or more big data ecosystem products and languages such as Spark, Snowflake, Databricks, etc. - Production experience in core data technologies and platforms (e.g., Spark, HDFS, Snowflake, Databricks, Redshift, Amazon EMR). - Complete software development lifecycle experience, including design, documentation, implementation, testing, deployment, and ongoing operations. - Excellent communication and presentation skills, with previous experience working directly with internal or external customers. - Participating in pre-sales or project scoping; as well as account growth / revenue generation with external clients. - Experience delivering projects for external or internal clients in a professional services or consulting environment. - Ability to break down complex problems into structured, actionable steps and drive them through to completion. - Strong written and verbal communication skills in English. - Comfort presenting technical solutions to external clients and facilitating discussions with both technical and business stakeholders. - Demonstrated ability to work effectively with distributed and cross-functional teams, including data scientists, engineers, and business stakeholders. - Proven track record of taking ownership, managing multiple priorities, and delivering high-quality work with minimal supervision. Education - Bachelor’s degree in Computer Science or a related technical field, or equivalent practical experience preferred. Preferred Qualifications - Experience in specific industry verticals or problem spaces where machine learning and data platforms are applied at scale (e.g., personalization, forecasting, risk modeling, operations optimization). - Hands-on experience with ecosystem technologies and cloud platforms such as Spark, Databricks, Snowflake, AWS, Azure, or GCP, and experience working with ML tooling such as AWS SageMaker, Azure ML, and MLflow, as well as libraries such as TensorFlow, Keras, scikit-learn, or H2O. - Prior experience working in global or remote teams and partnering across US, LATAM, and/or India. - Contributions to open source technology stacks, technical communities, speaking, or writing are a plus. - A Master’s or other advanced degree in data science, computer science, or a related field. Location & Time Zone Expectations - This role is based in the United States and operates primarily in the Central Time Zone. - We are a remote-first company, and you should be comfortable working with a distributed global team. - Some flexibility may be required to collaborate across time zones with colleagues and clients. - Client needs may occasionally require flexibility in working hours to support key milestones or workshops. Benefits - Remote-First Work Environment - 401k plan with company match - Dental and Vision insurance - Home Office Equipment Stipend - Annual stipend for Learning and Development - Competitive compensation, excellent benefits, 4 weeks PTO plan plus 10 Holidays (and other cool perks)