Senior Machine Learning Engineer

We are looking for a Senior Machine Learning Engineer to join our Machine Learning team. In this role, you will design, build, and operationalize production-grade machine learning solutions that turn data science models into reliable, scalable business capabilities. You will collaborate closely with clients, data scientists, data engineers, and platform teams to ensure models are deployable, maintainable, and aligned with customer environments. You will focus on robust infrastructure, deployment, and data integration to ensure our solutions deliver measurable impact in production.

• Own and drive end-to-end design, implementation, and production deployment of machine learning solutions for enterprise data and AI initiatives.
• Translate business requirements into technical and data engineering 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 environments and tooling that enable data scientists to build, train, and evaluate models efficiently and securely.
• Work within customer systems to extract, integrate, and prepare data for analytics and model development, ensuring quality, performance, and reliability.
• Define and implement deployment approaches and infrastructure for machine learning models so they can be consumed and maintained by the business.
• Develop and execute operational testing strategies, including QA validation, performance testing, and production rollout plans for models and supporting services.
• Ensure the quality, stability, and observability of delivered solutions through logging, monitoring, testing, and documentation.

• Collaborate with cross-functional partners including data science, data engineering, platform/DevOps, and business stakeholders to deliver successful client engagements.
• Provide technical leadership during discovery sessions, architecture and design reviews, and implementation phases to align on scalable MLOps patterns.
• Ensure high quality in deliverables through code reviews, technical documentation, automated testing, and adherence to security and governance standards.
• Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery patterns, and standardize machine learning deployment approaches.
• Work closely with data scientists to shape model integration patterns, data contracts, and performance requirements that enable deployment at scale in harmony with existing systems and pipelines.

• Contribute to internal initiatives such as IP development, MLOps accelerators, infrastructure templates, playbooks, and training for colleagues on best practices for deploying ML in production.
• Represent phData with professionalism in all interactions, communicating clearly with both technical and non-technical stakeholders.

You are a hands-on machine learning engineer and collaborative problem solver who enjoys turning models into robust, production-ready solutions. You are comfortable working across the stack—from data pipelines and infrastructure to APIs and monitoring—and partnering directly with data scientists, engineers, and business stakeholders. You thrive in an outcomes-driven environment, navigating complex customer ecosystems and helping teams deliver reliable, scalable machine learning capabilities.

• Experience in industry verticals or problem spaces where machine learning is applied to large-scale data (e.g., financial services, retail, manufacturing, healthcare, or similar).
• Hands-on experience with ecosystem technologies such as HBase, Impala, Solr, Kudu, Streamsets, NiFi, ElasticSearch, Databricks, Snowflake, or major cloud platforms (AWS, Azure, GCP).
• Experience with MLOps tooling, including services such as AWS SageMaker and frameworks such as MLflow.
• Prior experience working in global or remote teams and partnering across US, LATAM, and/or India.
• Contributions to open source projects, technical communities, speaking engagements, or technical writing related to data, ML, or MLOps are a plus.
• A Master’s or other advanced degree in data science, computer science, or a related field.

• 4+ years of experience in machine learning engineering, software engineering, or data engineering roles building and deploying ML solutions to production.

• Proficiency in modern programming languages such as Python, Scala, Java, or similar.
• Experience building and operating robust data pipelines and distributed data processing solutions using technologies such as Spark, Pandas, and SQL.
• Hands-on experience deploying data science/machine learning models using libraries and frameworks such as TensorFlow, Keras, scikit-learn, H2O, or similar.
• Strong working knowledge of SQL, including writing, debugging, and optimizing complex and distributed queries.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes (or similar).
• Familiarity with multiple data source systems and messaging/streaming technologies (e.g., JMS, Kafka, RDBMS, data warehouses, MySQL, Oracle, SAP).
• Systems-level knowledge of network and cloud architecture, operating systems (e.g., Linux), and storage/compute platforms (e.g., AWS, Databricks, Cloudera).
• Experience developing and operating APIs and web services using frameworks such as Flask, Django, or Spring (or equivalent).
• Demonstrated experience with the complete software development lifecycle, including design, implementation, testing, documentation, deployment, and ongoing operations.
• Strong analytical abilities; ability to translate business requirements and use cases into end-to-end data and ML solutions, including data ingestion, ETL processing, data access, consumption, and custom analytics.

• Experience delivering projects for external or internal clients in a professional services, product, 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, including the ability to explain technical concepts to both technical and non-technical audiences.
• Proven experience presenting solutions and working directly with internal and/or external customers.

• 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.
• Comfort working in customer environments, quickly understanding new systems, and adapting solutions to fit existing architectures and processes.

• Bachelor’s degree in Computer Engineering, Computer Science, or a related technical field, or equivalent practical experience preferred

This role is based in Latin America.

• 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.

• Impactful Work: Partner with leading organizations on meaningful data & AI initiatives.
• Collaborative Culture: Work with a supportive, high-performing global team that values transparency, autonomy, and continuous improvement.
• Growth Opportunities: Access to challenging projects, mentorship, and structured development pathways.