Lead AI/ML Engineer
Lead AI/ML Engineer defining architecture and strategy for enterprise AI/ML pipelines, mentoring teams, and integrating LLMs and Generative AI into production using Python, cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and MLOps tooling.
- Compensation
- Not disclosed
- Location
- Argentina
- Work setup
- Remote
- Seniority
- Lead
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Bachelor's or Master's degree in Computer Science, Engineering, AI, or related quantitative field
- 7 to 10 years of progressive experience in ML engineering or software development with an ML/AI focus
- Expert knowledge of ADK and other agentic frameworks with production experience
- Proven technical leadership experience including mentoring engineers and driving architectural choices
- Expert programming skills in Python
- Proficiency in ML/AI frameworks such as TensorFlow, PyTorch, or Scikit-learn
- Hands-on experience deploying and fine-tuning LLMs or similar GenAI models into low-latency production environments
- Extensive experience with cloud platforms (AWS, GCP, Azure)
- Container orchestration experience using Docker and Kubernetes
- Proficiency in Data Engineering principles and ETL/ELT processes
- Experience with version control systems (e.g., Git)
- Experience orchestrating complex ML pipelines using Kubeflow, MLflow, or managed cloud services
- Experience building and optimizing scalable, distributed AI/ML systems
- Deep familiarity with MLOps best practices including model monitoring, logging, explainability, and CI/CD for AI assets
- Strong communication and teamwork skills across cross-functional teams
Nice to have
- Ph.D. in a relevant field
Full description
Why you:
As a Lead AI / ML Engineer, you will define the technical vision, strategy, and overall architecture for Artificial Intelligence and Machine Learning solutions. Beyond building robust, scalable AI/ML pipelines, you will lead engineering teams, mentor junior and mid-level engineers, and drive technical decision-making across projects. You will take full ownership of integrating advanced models—including Large Language Models (LLMs) and Generative AI—into enterprise products, partnering closely with executive leadership, product management, and cross-functional engineering teams to deliver impactful business outcomes.
If this is you, and you wonder what it would be like to work at Pythian, reach out to us and find out!
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What you will be doing:
- Lead the architectural design and strategic development of high-performance AI and machine learning pipelines for complex enterprise initiatives.
- Provide technical leadership, mentorship, and guidance to AI/ML engineers and data scientists, fostering technical excellence and career growth.
- Deploy, manage, and scale diverse AI models, including pre-trained models (e.g., LLMs, vision models) and custom ML models, into production environments.
- Work with data scientists and researchers to operationalize models, transforming prototypes into resilient, production-grade AI systems that meet stringent business requirements.
- Optimize model performance, latency, and cost-efficiency across various cloud and edge deployment targets.
- Integrate AI/ML solutions with major cloud platforms (AWS, GCP, Azure) and leverage containerization technologies (Docker, Kubernetes) for consistent deployment.
- Implement comprehensive MLOps practices, including automated testing, continuous integration/continuous delivery (CI/CD) for models, model versioning, monitoring, and proactive maintenance systems.
- Drive stakeholder management and partner with senior leadership, product managers, and clients to align technical AI roadmaps with business strategy.
- Work closely with software engineering teams to ensure seamless and ethical integration of AI capabilities into larger applications and user workflows.
- Stay up to date with the latest advancements in AI/ML research, Generative AI, data engineering, and MLOps deployment strategies.
What we need from you:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence, or a related quantitative field. Ph.D. is a plus.