Requirements 5+ years of software engineering experience with at least 3 years in ML engineering in production environments. Strong expertise in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn, or equivalent). Hands‑on experience deploying ML models in enterprise environments. Experience integrating and operationalizing LLMs and RAG architectures. Solid understanding of CI/CD for ML systems. Experience with AWS, Azure, or GCP in enterprise contexts. Strong knowledge of APIs, distributed systems, and scalable architecture. Advanced English proficiency. Core Competencies Production-oriented engineering mindset. Ownership of system reliability and scalability. Adaptability to heterogeneous enterprise environments. Strong collaboration and client-facing capability. Proven enterprise production experience with ML systems. Hands‑on experience with LLM integration and RAG architectures. Strong software engineering fundamentals (APIs, scalability, testing, CI/CD). Responsibilities Design and implement end-to-end ML and GenAI systems, from development to production deployment. Develop scalable architectures for RAG‑based applications, LLM integrations, and advanced inference pipelines. Productionize machine learning and large language models, ensuring reliability, observability, and maintainability. Build and optimize data and model pipelines, including training, evaluation, deployment, and monitoring. Design model serving solutions and API integration patterns within enterprise systems. Optimize model performance for latency, throughput, cost efficiency, and resilience. Implement automated testing, evaluation frameworks, and guardrails for ML and LLM systems. Collaborate with Data Scientists to transition prototypes into production‑grade solutions. Engage directly with client technical teams across industries and technology stacks. #J-18808-Ljbffr