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

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Job Summary Synechron is seeking a highly experienced AI Technical Lead specializing in Generative AI to guide the development and deployment of advanced AI-powered solutions. 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 leverage extensive expertise in Python, ML frameworks, cloud platforms, and MLOps practices to support enterprise AI initiatives that drive innovation, operational efficiency, and strategic growth. Software Requirements Required Software Proficiency: Python (latest stable version, e.g., Python 3.8+) - in-depth experience developing and supporting AI/ML pipelines and automation tasks ML Frameworks: PyTorch, TensorFlow - strong hands-on experience in training, fine-tuning, and inference of large models Generative AI frameworks: Hugging Face Transformers, LangChain, OpenAI APIs - expertise in developing, prompt engineering, and deploying models Cloud Platforms: AWS, Azure, GCP - extensive experience deploying ML models, supporting model lifecycle management in cloud environments Model Management & Orchestration: MLflow, Kubeflow - supporting model versioning, monitoring, and continuous training workflows Data handling tools: Pandas, NumPy - for data preparation, feature engineering, and analysis supporting model performance Preferred Software Skills: AI model testing: support for automated model validation, bias detection, and performance evaluation tools Integration frameworks: support for REST APIs, gRPC, and other deployment tools supporting AI microservices Deployment automation: support for CI/CD pipelines using Jenkins, Azure DevOps, or GitLab supporting automated deployment and retraining Overall Responsibilities Lead the end-to-end development of AI models supporting enterprise use cases like NLP, retrieval-augmented generation (RAG), and multimodal AI solutions Build scalable, cloud-enabled AI pipelines supporting training, deployment, and continuous learning cycles Collaborate with data scientists, engineering, and product teams to translate business needs into AI solutions supporting operational and strategic goals Support model optimization for performance, scalability, and cost efficiency in enterprise environments Drive prompt engineering, fine-tuning, and evaluation strategies to enhance model effectiveness and fairness Implement model validation, bias mitigation, and compliance with AI ethics standards supporting responsible AI practices Automate model deployment and monitor model health, performance, and drift using cloud-native tools supporting MLOps Maintain documentation on model architecture, training data, evaluation reports, and operational procedures Technical Skills (By Category) Languages & Frameworks (Essential): Python: core language for model development and automation support ML Frameworks: PyTorch, TensorFlow supporting training and inference workflows Transformers and LangChain supporting large language model deployment Model Management & Data Handling: Pandas, NumPy supporting data processing and feature engineering Model versioning: MLflow, Kubeflow supporting deployment and lifecycle management Cloud & Infrastructure: AWS, Azure, or GCP (preferred) supporting cloud deployment, scaling, and monitoring Cloud-native ML services supporting large-scale training and inference (preferred) Tools & Automation: CI/CD support supporting automated model deployment, validation, and retraining pipelines Support for model explainability, bias detection, and monitoring tools Experience Requirements 7-12 years supporting enterprise AI/ML projects, including large language models and multimodal systems Proven experience designing, training, fine-tuning, and deploying scalable AI models supporting enterprise use cases Extensive expertise supporting AI model automation, versioning, monitoring, and compliance in cloud environments Experience working within regulated industries supporting responsible AI and data governance standards (preferred) Demonstrated success collaborating with data scientists, ML engineers, and product teams on enterprise AI solutions Day-to-Day Activities Develop, train, fine-tune, and deploy large language models, diffusion models, and multimodal AI solutions supporting enterprise applications Build automated data pipelines supporting training, validation, inference, and retraining for continuous learning Collaborate with ML teams and stakeholders to support model deployment, monitoring, and optimization workflows Conduct model evaluation, bias mitigation, and performance tuning to enhance fairness and operational quality Troubleshoot deployment issues, model drift, and inference latency challenges proactively Automate retraining, validation, and model management processes supporting MLOps best practices Document model architectures, training datasets, evaluation results, a

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