What are the responsibilities and job description for the AI/ML Platform Engineering Lead (Python) - Onsite position at Conexess Group?
Job Description/Preferred Qualifications
Join our AI/ML engineering team to build the backbone that powers production AI. As an AI/ML Platform Engineering Lead, you’ll architect and scale our MLOps infrastructure, enabling secure, reliable, and fast deployment of machine learning models across cloud and air gapped environments. This is a hands-on leadership role where you’ll mentor engineers, collaborate with hardware teams, and deliver AI solutions that transform semiconductor manufacturing.
Why This Role Is Compelling
Join our AI/ML engineering team to build the backbone that powers production AI. As an AI/ML Platform Engineering Lead, you’ll architect and scale our MLOps infrastructure, enabling secure, reliable, and fast deployment of machine learning models across cloud and air gapped environments. This is a hands-on leadership role where you’ll mentor engineers, collaborate with hardware teams, and deliver AI solutions that transform semiconductor manufacturing.
Why This Role Is Compelling
- Growth: You are empowered to make decisions to release product. Clear path for leadership and technical advancement in a fast-evolving AI domain with a strong and young team to help shape their careers.
- Impact: Ship AI solutions that directly reach customers and shape next-gen manufacturing.
- Design, deploy and scale AI/ML platforms for model training and inference.
- Implement CI/CD pipelines for models and data workflows, ensuring reproducibility and compliance.
- Develop tools and frameworks that empower ML engineers to move from prototype to production seamlessly.
- Collaborate with cross-functional teams—including hardware and software—to integrate AI into instruments and factory workflows.
- Establish observability and governance for model performance, drift detection, and reliability.
- Mentor engineers and champion best practices in platform engineering and MLOps.
- Strong programming skills in Python; experience with ML frameworks (PyTorch, TensorFlow, Keras).
- Expertise in MLOps tools (MLflow, Kubeflow), container orchestration (Kubernetes, Docker), and infrastructure automation (Terraform).
- Experience deploying models in secure or disconnected environments (air gapped, on-prem).
- Familiarity with cloud platforms (AWS, Azure, GCP) and hybrid architectures.
- Proven ability to lead technical design discussions and mentor engineering teams.
- Requires a minimum of 5 years of related experience with a Bachelors degree, or 3 years of experience and a Masters Degree