Generative AI Engineer - Python, Large Language Models & Cloud Deployment

About the role

Job Summary Synechron is seeking an experienced Generative AI Engineer to lead the development and deployment of AI-powered solutions supporting enterprise applications. This role involves designing, fine-tuning, and integrating large language models (LLMs), diffusion models, and transformers into scalable, production-ready systems. The ideal candidate will bring extensive expertise in Python, ML frameworks, cloud platforms, and MLOps tools, contributing to innovative, ethical, and efficient AI solutions that align with organizational goals. Software Requirements Required Software Proficiency: Python (latest stable version, e.g., Python 3.8+) - deep experience in ML pipelines, data processing, and automation ML Frameworks: PyTorch, TensorFlow - hands-on experience supporting training, fine-tuning, and inference of large models Generative AI frameworks: Hugging Face Transformers, LangChain, OpenAI APIs - expertise in model development, prompt engineering, and deployment support Cloud Platforms: AWS, Azure, or GCP - practical experience deploying ML models and supporting CI/CD pipelines in cloud environments MLOps tools: Docker, Kubernetes, MLflow - for model containerization, orchestration, versioning, and deployment support Data tools: Pandas, NumPy - experienced in data manipulation supporting model training and evaluation Preferred Software Skills: API integration: REST, gRPC support for external data and model interaction (preferred) Cloud-native services: Support for specialized ML services like AWS SageMaker, GCP Vertex AI (preferred) Automated testing frameworks supporting model validation and performance testing (e.g., pytest, Model Testing tools) Overall Responsibilities Design, develop, and fine-tune large language models, diffusion models, and transformers supporting enterprise AI initiatives Build scalable data pipelines and automation workflows supporting training, inference, and continuous learning cycles Collaborate with data scientists, platform engineers, and business stakeholders to translate use cases into operational AI solutions Support model deployment, versioning, and monitoring using containerization and MLOps practices Drive innovations in prompt engineering, model optimization, and AI ethics aligned with industry standards (e.g., fairness, transparency) Implement model validation, performance evaluation, and security practices to ensure compliance and operational safety Stay current with emerging AI research, frameworks, and cloud services, recommending improvements and new features Document model architecture, training processes, deployment procedures, and operational metrics Technical Skills (By Category) Languages & Frameworks (Essential): Python: core language supporting ML pipelines, automation, and scripting PyTorch, TensorFlow: deep learning frameworks supporting training and inference Transformers, LangChain, OpenAI APIs: model development, prompt engineering, and API-based integrations supporting enterprise solutions Data & Model Management: Data manipulation with Pandas, NumPy supporting training data setup and performance tuning Model versioning, artifact management supporting continuous deployment (MLflow, Model Registry) Cloud & Infrastructure: AWS, Azure, or GCP supporting scalable deployment of AI models (preferred) Cloud-native ML services support supporting large-scale training and inference (preferred) Tools & Platforms: Docker, Kubernetes supporting containerized model deployment CI/CD pipelines supporting automated testing, deployment, and performance monitoring in cloud environments Security & Governance: Knowledge of data privacy, model explainability, and fairness standards supporting ethics and compliance Experience Requirements 5-10 years of professional experience in ML/AI pipeline development, training, and deployment supporting enterprise applications Hands-on experience with large language models, diffusion models, transformers, and prompt engineering support Proven expertise in cloud deployment, containerization, and MLOps best practices supporting scalable, service-driven AI solutions Prior experience supporting AI ethics, model audits, bias mitigation, and compliance in regulated industries (preferred) Demonstrated success working with cross-functional teams and translating business needs into technical AI solutions Day-to-Day Activities Develop and fine-tune large language models, diffusion models, and transformers supporting enterprise application needs Build and automate ML pipelines supporting training, inference, and model updates using cloud and containerized solutions Collaborate with data scientists, platform engineers, and business units to deploy, monitor, and improve AI models Conduct model validation, bias detection, and performance evaluation supporting AI governance and compliance Troubleshoot model performance issues, optimize inference speed, and ensure scalable deployment Integrate models with e

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