Job Description

You will design, build, and deploy production-grade machine learning systems that solve real business challenges. Working closely with cross-functional teams, you'll transform innovative ideas into scalable AI solutions. Core Responsibilities -. Architect and implement end-to-end machine learning solutions, from proof of concept to production deployment. Design and develop ML pipelines using modern frameworks (TensorFlow, PyTorch, scikit-learn) for applications in NLP, computer vision, and predictive analytics. Lead the integration of Large Language Models (Vertex AI Gemini, GPT-4) into production systems, including prompt engineering and response optimization. Build robust MLOps infrastructure for model monitoring, versioning, and automated retraining. Collaborate with product teams to translate business requirements into technical specifications. Technical Requirements -. Strong software engineering foundation with production-level Python experience. Expertise in ML frameworksTensorFlow/PyTorch, scikit-learn, and modern ML libraries. Proven experience with ML infrastructure and deployment:. —‹Model serving and scalability. —‹Containerization (Docker) and orchestration (Kubernetes). —‹CI/CD pipelines for ML workflows. —‹Cloud platforms (AWS/Azure/GCP). Proficiency in data processing and analysis. —‹Data preprocessing and feature engineering. —‹Performance optimization and debugging. —‹Big data technologies (Spark, distributed computing). Experience with LLM integration and optimization:. —‹Prompt engineering and chain-of-thought implementations. —‹RAG (Retrieval Augmented Generation) architectures. —‹Vector databases and semantic search. MLOps & Best Practices. Experience with ML monitoring and observability tools. Understanding of A/B testing and experimentation frameworks. Knowledge of model versioning and reproducibility. Familiarity with ML security and ethical considerations. Ideal Background. Bachelor's/Master's in Computer Science, Engineering, or related field. 5+ years of software engineering experience. Strong track record of deploying ML systems to production. Experience mentoring junior engineers and contributing to technical discussions. What Sets You Apart. Open-source contributions to ML/AI projects. Experience with distributed training and model optimization. Publication record in ML/AI conferences or journals. Domain expertise in NLP, computer vision, or recommendation systems. Show more Show less