Sr. Machine Learning Engineer
Design, build, and operationalize production AI/ML systems for enterprise clients, translating architectures into clean Python and SQL code, integrating with cloud data platforms and APIs, and contributing to reusable accelerators and engineering standards.
- Compensation
- Not disclosed
- Location
- Brazil
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 5+ years of experience in AI/ML engineering, software engineering, data engineering, or related, with at least 3 years designing, building, deploying, or operating AI/ML solutions in production
- Strong programming experience in Python for building production-grade AI, ML, and data pipelines
- Strong working knowledge of SQL for querying, transforming, and validating data used in AI and ML solutions
- Practical experience with modern cloud, data, and AI ecosystems such as Snowflake, Databricks, AWS, Azure, Google Cloud, dbt, Anthropic, OpenAI, or comparable technologies
- Understanding of production AI/ML concerns including evaluation, observability, security, governance, scalability, performance, and operational support
- Experience developing production software, APIs, services, data pipelines, or AI applications and integrating them into enterprise environments
Nice to have
- Experience building agentic AI systems, including orchestration, tool use, planning, memory, multi-agent patterns, or protocols such as MCP
- Experience designing and deploying enterprise RAG systems, semantic retrieval, AI applications, or intelligent workflow automation
- Experience with cloud-native AI/ML services such as AWS Bedrock, SageMaker, Azure AI/ML, Google Vertex AI, Snowflake Cortex, and LLM orchestration frameworks like LangChain or LlamaIndex
- Experience with model registries, feature stores, data and model lineage, model monitoring, drift detection, AI evaluations, prompt management, AI gateways, and CI/CD for AI/ML workloads
- Experience building reusable accelerators, modular architectures, or libraries deployed across multiple clients, and contributions to open-source projects or technical communities
Full description
Join phData, a remote-first data and AI consultancy company with employees across the United States, Latin America, and India. We partner with industry leaders, including Snowflake, AWS, Anthropic, Glean, and dbt, to solve the complex data and AI challenges that slow large enterprises.
We're growing fast, and we give our people real ownership over their work. We hire top performers and trust them to deliver results.
Why phData?
- Snowflake Implementation Partner of the Year — 7 consecutive years, and 2026 Snowflake AI Partner of the Year
- AWS Premier Tier Services Partner — the highest tier of recognition in the AWS Partner Network
- 2025 Fivetran Partner of the Year (4th consecutive year)
- 2025 dbt Labs Partner of the Year (3x winner) with Visionary partner status
- 2026 KNIME Customer Excellence Partner of the Year
- Preferred Partner in the Anthropic Claude Partner Network
- #1 Partner in Snowflake Advanced Certifications
- 600+ Expert Cloud Certifications (Sigma, AWS, Azure, Dataiku, and more)
- Recognized as an award-winning workplace in the US, India and LATAM
We are looking for a Senior Machine Learning Engineer to join our Applied AI team. In this role, you will design, build, and operationalize production-ready AI and machine learning systems that solve meaningful business problems for enterprise customers. You will help move solutions from experimentation into scalable production, while contributing to reusable accelerators and engineering standards. You will collaborate closely with Applied AI Solutions Architects, data scientists, data engineers, platform engineers, project stakeholders, and client representatives to deliver high-quality solutions and advance phData's delivery excellence.
Key Responsibilities
- Build and operationalize production-ready Applied AI and machine learning solutions that align with customer objectives, technical requirements, and enterprise environments.
- Translate solution architectures and business requirements into clean, maintainable, well-tested code, data and model pipelines, and technical deliverables for AI applications.
- Implement robust production patterns for predictive ML, generative AI, LLM applications, agentic workflows, and intelligent automation, including evaluation, monitoring, and guardrails.
- Integrate AI solutions with enterprise data platforms, applications, APIs, and workflows, ensuring reliability, security, performance, and cost-effectiveness in production.
- Contribute to reusable accelerators, reference implementations, engineering standards, and technical playbooks that improve how phData delivers Applied AI solutions across clients.