This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description This role is a senior technical and strategic leadership position focused on architecting, scaling, and operationalizing modern data and machine learning platforms. - Lead global, distributed teams to deliver robust cloud-native data solutions and intelligent AI/ML-powered products that drive measurable business outcomes. - Combine hands-on technical execution with strategic vision, enabling data-driven transformation across diverse industries. - Partner with cross-functional teams to shape data strategy, embed ML in applications, and ensure solutions are scalable, secure, and high-performing. - Define best practices, implement cutting-edge technology, and shape a culture of innovation and continuous learning. Accountabilities - Lead and mentor global, distributed Data Engineering and Machine Learning teams, fostering a culture of trust, experimentation, and growth. - Architect and oversee cloud-native data platforms, pipelines, and streaming systems on AWS, Azure, or GCP using technologies such as Databricks, Snowflake, Redshift, BigQuery, Spark, Kafka, Airflow, dbt, and Kubernetes. - Define and execute responsible ML strategies, integrating AI/ML into applications, analytics, and automation for impactful client outcomes. - Develop modular engineering frameworks, runbooks, and tooling standards to optimize scalability, observability, and security. - Partner with Sales, Partnerships, and Account Teams to co-create data & ML solutions that align technical execution with business objectives. - Translate complex client and business needs into solution designs, proposals, and technical collateral, serving as a trusted advisor in workshops and pitches. - Collaborate across product, engineering, and operations teams to deliver integrated, end-to-end data and ML solutions. Qualifications - 15+ years of experience in data engineering, software engineering, or related technical roles, with 7+ years leading Data Engineering or ML teams. - Hands-on experience with cloud data platforms, big data toolchains (Spark, Kafka), data orchestration tools (Airflow, dbt), and ML frameworks (SageMaker, Azure ML, TensorFlow). - Proven experience in designing and scaling modern, cloud-native data architectures and pipelines. - Strong business acumen with success in pre-sales, solutioning, and growing data/ML engagements. - Excellent leadership, mentoring, and team-building skills, with the ability to foster inclusive, high-performing engineering cultures. - Exceptional communication and collaboration skills, able to work effectively across technical, executive, and client-facing environments. - Demonstrated ability to translate complex technical capabilities into business impact and client solutions. Preferred Qualifications - Experience in consulting or global delivery contexts. - Knowledge of Agile/Lean development, DataOps, MLOps, and CI/CD for data pipelines and models. - Track record of creating enablement programs, learning paths, and team development initiatives. Benefits - Base salary range: $200,000—$240,000 USD. - Equity opportunities and potential performance-based bonuses. - Flexible remote-first work model with optional co-working spaces globally. - Premium health, dental, and vision insurance for employees, with subsidized coverage for dependents. - Flexible Time Off (PTO) and company holidays. - 401(k) retirement plan. - Professional development opportunities, including access to licensed Coursera courses and paid certifications. - Employee referral programs and global office access program. - Collaborative, inclusive culture emphasizing autonomy, mastery, and continuous learning.

Role Description LLM Product Development - At least 1-2 years of professional experience building and deploying conversational AI chat and/or voice agents. - Familiarity with prompt engineering techniques and strategies for optimizing LLM outputs. - Understanding of agentic frameworks and architectures, vector databases, and retrieval-augmented generation (RAG) techniques, MCP Servers, and writing evals. AWS Cloud Engineering - 5 years of experience developing and deploying scalable solutions on AWS cloud services using a wide range of services (e.g., EC2, S3, RDS, Lambda, VPC, IAM). - Managing infrastructure-as-code with CDK or CloudFormation. - Design, develop, and implement robust, scalable, and secure cloud infrastructure solutions. Knowledge of Gen AI-related tools and libraries - You know your way around libraries such as LangChain, LlamaIndex, and Pydantic. - Bonus points if you’re familiar with tools such as ElevenLabs and n8n. Python mastery - Proficiency in Python and relevant AI/ML frameworks (e.g., TensorFlow, PyTorch, Hugging Face Transformers). - Experience with Golang is desired. Problem-Solving Skills - Excellent analytical and problem-solving abilities, with a focus on practical and efficient solutions. Collaboration & Communication - Strong communication and interpersonal skills, capable of working collaboratively with cross-functional teams and effectively communicating technical concepts to non-technical team members. Educational Background - Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field. - Strong knowledge of mathematics and algorithm development. Language Proficiency - Fluency in Spanish and English, both written and spoken.

• Develop, fine-tune, and deploy **AI models** in production environments • Integrate AI features into iOS applications using **Swift and CoreML** • Build backend services to support AI-driven functionality • Manage hosting environments and maintain running instances (e.g., **Oracle Cloud infrastructure**) • Set up, deploy, and maintain servers that support AI workloads • Optimize performance, scalability, and reliability of AI systems • Collaborate with product and engineering teams to design innovative AI-powered solutions • Take ownership of technical challenges and independently drive solutions

• Architect and scale the core intelligence behind the platform • Design, build, and optimize the pipelines and agent systems that drive live customer interactions • Partner with engineering leadership to take ideas from whiteboard to production quickly • Design and optimize production-grade RAG systems • Build ranking, scoring, and routing models for live inference • Deploy and monitor reinforcement learning systems • Build and maintain ML APIs and microservices using Docker and Kubernetes