Job Description

Job Overview (Primary Skills - GCP, Kubeflow, Python, Vertex.ai) As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with data science leadership and stakeholders to understand business objectives, map the scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that the client aspire to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision.