GenAI Engineer – LLM, RAG, Agentic AI & MLOps

Job Summary Synechron is seeking a seasoned Gen AI Engineer to lead the development and deployment of agentic AI solutions supporting enterprise business processes. This role involves designing, fine-tuning, and implementing large language models (LLMs), retrieval-augmented generation (RAG) systems, and multimodal agents, with a focus on delivery, performance, and operational security. The ideal candidate will leverage extensive experience in Python, cloud platforms, and AI frameworks, collaborating across teams to innovate and provide scalable, secure AI solutions that support business growth. Software Requirements   Required Software Proficiency: Python (latest stable version, e.g., Python 3.8+) — extensive hands-on experience supporting training, fine-tuning, and inference of large AI models (supporting 5+ years) AI Frameworks: PyTorch, TensorFlow — proven expertise in training, deploying, and optimizing deep learning models supporting generative and multimodal capabilities Large Language Models: GPT, Claude, Llama, Gemini, or similar — experienced in prompt engineering, fine-tuning, and deployment support (supporting 3+ years) Cloud Platforms: AWS, Azure, or GCP — experience deploying and managing scalable AI models supporting enterprise solutions (preferred support, 3+ years) Model orchestration & management: MLflow, Kubeflow supporting model lifecycle, versioning, and monitoring (preferred support) Data processing: Pandas, NumPy supporting data preparation and feature engineering support Preferred Software Skills: AI model evaluation and bias mitigation tools supporting model fairness and performance assessment MLOps pipelines supporting continuous deployment, retraining, and automation support (Kubeflow, TFX, or similar) Multi-modal processing frameworks supporting text, images, and audio inputs (preferred) Overall Responsibilities Lead the design, training, and deployment of large language models and multimodal agents supporting enterprise automation and insights Develop scalable AI pipelines supporting real-time inference, retraining, and model monitoring in cloud environments Collaborate with data scientists, platform engineers, and business stakeholders to translate use cases into operational AI systems supporting automation and decision support Support prompt engineering, model evaluation, bias detection, and performance tuning for operational reliability and fairness Automate deployment, versioning, and monitoring workflows supporting MLOps and responsible AI standards Conduct model validation, interpretability checks, and security assessments supporting compliance in regulated environments Support enterprise data pipelines supporting multimodal, retrieval-augmented, and knowledge-based AI systems supporting operational transparency Document model architecture, training, tuning, deployment procedures, and operational metrics supporting audit and compliance regimes Technical Skills (By Category) …