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Lead Machine Learning Engineer (ML Ops)

Lead Machine Learning Engineer (ML Ops)

IFSWarsaw, Masovian Voivodeship, PL
Ponad 30 dni temu
Opis pracy

Job Description

This role is all about hands-on technical prowess. You’ll be in the driver’s seat, working with autonomy, accountability, and technical brilliance. Your mission includes :

  • Identify High-Value AI / ML Prospects :  Spot high-value AI / ML opportunities within our product offerings.
  • Guide AI / ML Technology Trends :  Serve as the AI / ML expert, guiding the team towards the latest and greatest technology trends. Shape the roadmap by providing estimates and implementation strategies.
  • Develop and Integrate Data Science Projects :  Craft and integrate data science projects from the ground up. From framing problems and experimenting with proofs of concept to the grand finale of implementation, ensure scalability and top-tier performance.
  • Collaborate with Cross-Functional Teams :  Work closely with Data Engineers, ML Engineers, Software Engineers, Solution Architects, and Product / Program Managers to define, create, deploy, monitor, and document ML models that are both tailored and industry-leading.
  • Evangelize AI / ML Technology :  Become an AI / ML evangelist. Shine on the conference stage, host webinars, and pen compelling white papers and blogs. Share discoveries with clients and internal stakeholders, offering actionable insights that drive change.

Qualifications

  • Experience :   5+ years of machine learning (ML) expertise for Lead, demonstrated through successfully completed projects.
  • AI / ML Solutions :  Expertise in bringing AI / ML solutions to production, including scoping, design, development, testing, deployment, and monitoring.
  • Cloud ML Solutions :  Knowledge in creating and delivering Cloud ML solutions at scale using Docker, Kubernetes, and cloud services (, AWS, Azure, GCP).
  • Model Deployment :  Experience with model deployment and serving tools like Seldon Core and KServe.
  • Model Monitoring :  Expertise in monitoring models and evaluating metrics to drive optimizations using tools like Elastic Stack, Prometheus, and Alibi Detect.
  • Software Engineering and DevOps :  Solid background in software engineering, DevOps, and MLOps practices, with familiarity in managing infrastructure as code using tools like Terraform and Helm Charts.
  • Scripting :  Expertise in scripting (shell) to automate tests and procedures, ensuring reliability and validity of code repositories.
  • Pipeline Orchestration :  Proficiency with pipeline orchestration tools, such as Airflow, Kubeflow, and Argo Workflows.
  • Programming Skills :  Proficiency in Python, C#, and ML libraries and tools such as Semantic Kernel, LangChain, Keras, TensorFlow, and PyTorch.
  • ML Experiment Tracking :  Familiarity with ML experiment tracking and collaboration tools, such as MLflow and Weights & Biases.
  • Data Handling :  Skills in data handling and feature engineering, with the ability to use big data technologies such as Spark, Dask, and Polars.
  • Communication Skills :  Outstanding communication skills, with the ability to convey complex technical concepts to non-technical stakeholders and work effectively with cross-functional teams.
  • Soft Skills :  A results-driven attitude, a passion for innovation, and a proactive, self-starting nature. Strong organizational skills, capable of juggling multiple tasks, and a strategic thinker always on the hunt for the next big thing.
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