Senior ML/AI Engineer

Senior ML/AI Engineer We are seeking a highly skilled and visionary Senior ML/AI Engineer to lead the architecture, development, and optimization of our machine learning infrastructure and MLOps ecosystem. This role is ideal for a hands‑on technical leader who thrives at the intersection of software engineering, data science, and systems architecture. You will play a pivotal role in shaping our next‑generation AI capabilities—designing scalable, cloud‑agnostic ML platforms, productionizing complex models, and driving innovation in automation, deployment, and performance optimization. The ideal candidate is passionate about operational excellence, cutting‑edge ML engineering practices, and mentoring teams to build world‑class AI solutions that power real business impact. Responsibilities Architect and lead the design of the end‑to‑end MLOps platform and scalable ML serving infrastructure (e.g., cloud‑agnostic solutions, custom model serving frameworks). Set coding standards, best practices, and design patterns for ML engineering across the team. Act as a technical mentor to junior and mid‑level engineers. Own and optimize the highest‑volume, most complex ML pipelines for training and inference. Drive initiatives to achieve ultra‑low latency and high‑throughput model serving. Evaluate and select core technologies (e.g., Kubernetes, serverless, specialized hardware) for the ML platform. Manage infrastructure cost optimization and security at a platform level. Design and implement advanced monitoring systems that track model drift, concept drift, and data lineage. Establish automated model testing and canary release strategies. Lead technical collaboration with Data Scientists, Data Engineers, and Core Software Engineers to define integration contracts and productionize models across multiple business domains. Define the enterprise standards for model registry, artifact management, and data versioning to ensure complete experiment and production reproducibility. Streamline and reduce the cycle time for moving a Data Science prototype from research to a fully monitored and stable production service. Skills and Experience Exceptional proficiency in Python (including advanced libraries like Pandas, NumPy, Scikit‑learn) and/or Scala/Java for building high‑performance, production‑quality systems. Deep practical experience leveraging services within at least one major cloud ecosystem (e.g., AWS, Azure, GCP) or a platform like Databricks for production deployment and scaling. Deep, hands‑on experience with major machine learning and deep learning frameworks (e.g., TensorFlow, PyTorch). Proven track record in designing and implementing end‑to‑end MLOps systems using open‑source tools. Expertise with workflow management tools like Apache Airflow, Kubeflow etc. Mastery of Docker and Kubernetes (K8s) for packaging, scaling, and managing containerized ML services across any environment. Extensive experience building scalable data pipelines leveraging distributed processing frameworks such as Apache Spark (PySpark/Scala). Ability to architect cloud‑agnostic ML systems, including designing low‑latency API serving layers, real‑time inference mechanisms, and event‑driven architectures. Leadership and Soft Skills Demonstrated ability to evaluate new technologies and frameworks, and define the technical roadmap for the ML platform and MLOps practices. Proven experience mentoring junior engineers, conducting technical reviews, and driving team‑wide adoption of engineering best practices. Exceptional ability to communicate complex technical concepts to non‑technical stakeholders (e.g., product managers, business leaders) and translate business needs into technical designs. Superior analytical skills for root cause analysis of production system failures and complex performance bottlenecks. Education and Experience Experience: 7+ years of experience in machine learning, software engineering, or related fields, with at least 3 years focused on building and scaling production ML systems. Education: Bachelor’s or Master’s degree in Computer Science, Data Science, or a quantitative field is preferred. Seniority Level Mid‑Senior level Employment Type Full‑time Job Function Other and Information Technology Industries IT System Data Services and IT System Operations and Maintenance #J-18808-Ljbffr