Lead the design, planning, estimation, and coordination of AI/ML initiatives across multiple releases.
Take ownership of the entire lifecycle of AI/ML systems, from experimentation to production.
Partner with Product and Engineering teams to integrate AI/ML capabilities into company-wide projects.
Provide strategic technical guidance on MLOps frameworks and practices, tools, and best practices.
Foster a collaborative environment by communicating effectively across technical and non-technical teams.
Proactively identify challenges in AI/ML workflows and propose innovative, scalable solutions.
Drive initiatives to improve model accuracy, scalability, and reliability while adhering to privacy, security, and compliance standards.
Ensure comprehensive documentation of models, pipelines, algorithms, and system architecture.
Requirements
7+ years of experience in AI/ML engineering, including MLOps, AI infrastructure, building, deploying and maintaining machine learning models in production environments.
3+ years of technical leadership experience, with a track record of guiding teams through complex AI/ML projects.
Expert proficiency in programming languages such as Python, Java or Rust, and Infrastructure-as-Code with focus on MLOps, AI infrastructure design and implementation, data engineering, pipelines automation, machine learning, models evaluation and support of live production AI systems.
Deep understanding of AI/ML systems architecture, including experience with distributed systems and large-scale data pipelines.
Strong expertise in deploying and managing AI/ML models in cloud environments, with hands-on experience using AWS services such as Amazon Bedrock, SageMaker, and related tools, or alternatives in GCP (Vertex AI) or Azure (AI Studio) cloud.
In-depth knowledge of model serving infrastructure, machine learning algorithms and frameworks, including experience with LLMs, AI agentic patterns, and fine-tuning pre-trained models.
Experience with microservices architecture and CI/CD pipelines / MLOps for AI/ML model deployment, ensuring scalability, reusability, and testability.
Experience with agentic AI coding and orchestration tools for software engineering, such as Claude Code, Codex, or similar.
Proven ability to mentor and guide engineers, fostering growth and technical excellence without formal direct reporting relationships.
Strong collaboration skills, working cross-functionally with Engineering, DevOps, and Product teams to deliver impactful AI/ML solutions.
Experience with Agile/Scrum methodologies, effectively managing sprints and delivering iterative improvements.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related technical field.