Overview

Synechron is seeking a seasoned Gen AI Engineer to lead the development and deployment of agentic AI solutions supporting enterprise business processes.

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
• 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)
• 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 deployment environments supporting enterprise scale
• Automate model deployment, monitoring, and retraining pipelines supporting continuous delivery and performance tuning
• Document AI architecture, models, training, and operational procedures supporting audit readiness and governance

Qualifications

• 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)
• Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related technical fields
• 4+ years supporting enterprise AI/ML solutions, including large language models, retrieval-augmented systems, and multimodal agents
• Certifications supporting cloud platforms (AWS, GCP, Azure) or responsible AI practices are advantageous (preferred)
• Proven experience supporting or leading compliant, scalable AI systems supporting data privacy and fairness standards
• Strong analytical and troubleshooting skills supporting complex model validation, bias detection, and model performance issues
• Ready to work in hybrid model , for 3 days a week at client office.
• Leadership qualities to guide and mentor junior AI engineers and promote best practices in responsible AI deployment
• Stakeholder communication skills supporting requirement translation, reporting, and compliance documentation
• Adaptability supporting evolving AI research, cloud services, security, and regulatory standards
• Strategic thinking supporting scalable, secure, and ethical AI systems supporting enterprise objectives
• Organisational skills to manage multiple models, retraining cycles, and deployment activities efficiently in complex environments