GenAI Engineering

About the role

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 5-10 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) Languages & Frameworks (Essential): Python supporting large-scale model training, fine-tuning, and scripting for automation PyTorch and TensorFlow supporting deep learning model development and deployment Supporting libraries: Hugging Face Transformers, LangChain, support for RAG architecture and plugin integration Data & Model Management: Pandas, NumPy supporting data preparation, feature engineering, and validation Model versioning tools: MLflow, Kubeflow supporting lifecycle management and deployment support Cloud & Infrastructure: AWS, Azure, or GCP supporting scalable deployment and inference in enterprise settings (preferred) Container orchestration support: Docker, Kubernetes supporting scalable, cloud-native AI systems Model Evaluation & Monitoring: Tools supporting bias detection, fairness assessment, and inference monitoring (e.g., TensorBoard, custom dashboards) Experience Requirements 4+ years supporting enterprise AI/ML projects, including large language models, retrieval-augmented generation, and multimodal systems Proven experience in deploying AI models supporting automation, knowledge management, and operational workflows Extensive hands-on expertise in cloud AI deployment, orchestrating model lifecycle, and scalable inference support (preferably in regulated environments) Experience supporting responsible AI practices, model fairness, and security in enterprise settings Day-to-Day Activities Develop, fine-tune, and deploy large language models and multimodal agents supporting enterprise automation workflows Build and automate AI pipelines supporting training, inference, retraining, and model monitoring workflows in a cloud environment Collaborate closely with data scientists, platform teams, and business units to deliver scalable AI solutions supporting operational efficiency Conduct bias, fairness, and security evaluations supporting compliance and trustworthy AI practices Troubleshoot and optimize model inference latency, retraining workflows, and d

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