Employer description & source details
𝗞𝗲𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 Design, prototype, and validate end‑to‑end machine learning workflows from data ingestion to inference. Develop and fine‑tune agentic AI models that autonomously adapt to changing user contexts. Build cloud‑native microservices and APIs to expose ML capabilities for internal and external consumption. Integrate ML models with enterprise platforms such as data lakes, Kubernetes clusters, and event‑driven architectures. Ensure robust model governance, monitoring, and continuous improvement cycles. Collaborate with data engineering to optimize data pipelines and feature stores. Mentor and guide junior engineers on best practices, coding standards, and model deployment strategies. Conduct performance benchmarking, A/B testing, and scaling studies to meet SLAs. Lead the selection and evaluation of cutting‑edge ML frameworks and cloud services. Document architecture, code, and operational procedures for maintainability and knowledge transfer. 𝗪𝗵𝗮𝘁 𝗠𝗮𝗸𝗲𝘀 𝗬𝗼𝘂 𝗮 𝗚𝗿𝗲𝗮𝘁 𝗙𝗶𝘁 10+ years of experience in machine learning and data science with a proven track record of production deployments. Deep expertise in building agentic AI solutions using reinforcement learning, NLP, or multimodal models. Proficiency in cloud platforms (AWS, Azure, GCP) and container orchestration (Kubernetes). Strong background in developing RESTful and gRPC APIs and integrating them with external systems. Hands‑on experience with CI/CD pipelines, automated testing, and model deployment tools. Excellent coding skills in Python, Java, or Go, with familiarity in scalable frameworks such as TensorFlow, PyTorch, or JAX. Demonstrated ability to translate business requirements into technical designs and measurable outcomes. Experience mentoring teams, conduct
Extracted skills: python, java, go, aws, azure, gcp, kubernetes, machine learning, security