AI Model DevOps & Architecture Lead - Large Language Models, Cloud Deployment & Enterprise AI Solutions

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or a related technical field
• 7+ years supporting AI/ML model development, deployment, and lifecycle management in ent

Benefits

Additional Information

Job Summary Synechron is seeking a highly experienced AI Technical Specialist to spearhead and coordinate AI projects across development, testing, and deployment phases. This role involves identifying impactful AI use cases, leading hands-on implementation of AI models, and ensuring seamless integration into existing systems and workflows. The ideal candidate will stay abreast of the latest advancements in AI platforms and large language models (LLMs), providing technical mentorship and strategic guidance to foster innovation and operational excellence in enterprise AI initiatives. Software Requirements Required Software Proficiency: Python (latest stable version, e.g., Python 3.8+) - extensive experience in developing, testing, and refining AI models Frameworks: TensorFlow, PyTorch - proven expertise supporting model training, tuning, and production deployment Cloud platforms supporting AI workloads: AWS, GCP, or Azure - experience deploying AI models in cloud environments (preferably) Data management tools: SQL, NoSQL databases supporting data preparation, storage, and retrieval for AI projects Version control: Git/GitHub - strong collaboration and code management skills Preferred Software Skills: Large Language Models (LLMs) such as GPT, BERT, or similar - hands-on experience in fine-tuning, prompt engineering, and deployment support AI orchestration tools: MLflow, Kubeflow - for model lifecycle management supporting MLOps best practices Automation tools supporting CI/CD pipelines for AI solutions (e.g., Jenkins, Azure DevOps) Overall Responsibilities Lead end-to-end AI initiatives-from use case identification to model deployment and support-aligning solutions with organizational objectives Develop, train, and refine AI models supporting business challenges, focusing on accuracy, scalability, and efficiency Collaborate with cross-functional teams, including product managers, data scientists, and DevOps, to integrate AI capabilities seamlessly Stay current with emerging AI advancements, evaluating new models, tools, and platform capabilities for enterprise applicability Conduct model performance evaluation, tuning, and validation to meet quality standards and operational parameters Mentor team members on AI best practices, code development, and model management strategies Support the implementation of MLOps pipelines supporting model versioning, monitoring, and continuous improvement Document technical requirements, model architecture, deployment procedures, and operational guidelines supporting compliance and audit readiness Technical Skills (By Category) Languages & Frameworks (Essential): Python: core language supporting development, training, and inference of AI models TensorFlow, PyTorch: for training, tuning, and deploying deep learning models Models & Data Management: Experience working with large datasets, data pre-processing, and managing models' lifecycle (training, validation, deployment) Cloud & Infrastructure: AWS, GCP, or Azure supporting cloud deployment of AI models (preferred) Support for cloud-native AI services and infrastructure automation supporting scalability and reliability Tools & Platforms: Model development and management: MLflow, Kubeflow (preferred) CI/CD automation supporting model deployment, monitoring, and retraining processes Security & Governance: Knowledge of ML model security, data privacy standards, and compliance support (e.g., GDPR, HIPAA) Experience Requirements 7+ years of experience leading AI model development, deployment, and optimization in enterprise environments Proven track record of supporting large-scale AI solutions, including LLM fine-tuning and deep learning models Strong experience supporting AI model lifecycle management and deploying models in cloud environments Demonstrated leadership in coaching teams, fostering innovation, and implementing best practices in AI/ML workflows Experience in regulated industries such as finance, healthcare, or enterprise technology environments is preferred Day-to-Day Activities Lead the development, testing, and deployment of AI models supporting business use cases Collaborate with data scientists, developers, and DevOps teams to support model training, evaluation, and productionization Support the automation of model deployment pipelines, monitoring, and retraining workflows supporting MLOps practices Conduct model performance evaluation, interpretability analysis, and calibration to ensure accuracy Troubleshoot issues related to data, model inference, and deployment environments Evaluate new AI models, frameworks, and cloud services to optimize enterprise capabilities Document model architecture, deployment plans, and operational procedures supporting compliance and ongoing support

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