Sr. Machine Learning Solutions Architect

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

Client Delivery

• 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.

About You

You are a technical leader and client-focused consultant who enjoys turning complex machine learning ideas into robust, production-ready solutions. You are comfortable working across data, infrastructure, and application layers, partnering directly with data scientists, engineers, and business stakeholders. You thrive in an outcomes-driven environment, navigating complex customer ecosystems to design architectures that are performant, secure, scalable, and maintainable.

Required Qualifications

Experience

• 6+ years of experience as a Machine Learning Engineer, Software Engineer, or Data Engineer building and deploying production data and machine learning solutions.

Technical / Functional Skills

• 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.

Consulting / Delivery Skills

• 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.

Collaboration & Ownership

• 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

Preferred qualifications help candidates stand out but are not required for success in this role.

• Experience in specific industry verticals or problem spaces where machine learning