GenAI Engineer – LLM, RAG, Agentic AI & MLOps

About the role

Synechron is looking for a seasoned GenAI Engineer in Bengaluru to lead the design, fine-tuning and deployment of agentic AI solutions that support enterprise business processes. The role covers large language models (LLMs), retrieval-augmented generation (RAG) systems and multimodal agents, with a focus on delivery, performance and operational security.

Key responsibilities

• Lead the design, training and deployment of LLMs and multimodal agents for enterprise automation and insights.
• Build scalable AI pipelines for real-time inference, retraining and model monitoring in the cloud.
• Work with data scientists, platform engineers and business stakeholders to turn use cases into operational AI systems.
• Support prompt engineering, model evaluation, bias detection and performance tuning.
• Automate deployment, versioning and monitoring workflows under MLOps and responsible AI standards.
• Run model validation, interpretability checks and security assessments in regulated environments.
• Document architecture, training, tuning and deployment procedures for audit and compliance.

Required profile

• Bachelor's or Master's degree in Data Science, Computer Science, AI or a related field.
• 5+ years of enterprise AI/ML experience, including LLMs, RAG and multimodal systems.
• Proven experience deploying AI models and orchestrating model lifecycles, preferably in regulated environments.
• Experience with responsible AI, model fairness and security in enterprise settings.
• Willingness to work hybrid, three days a week at the client office.
• Leadership qualities to mentor junior engineers and stakeholder communication skills.

Required skills

• Python (3.8+) for model training, fine-tuning and inference.
• PyTorch and TensorFlow; Hugging Face Transformers and LangChain.
• Pandas and NumPy for data preparation and feature engineering.
• Cloud platforms (AWS, Azure or GCP) and model orchestration tools such as MLflow and Kubeflow.
• Container orchestration (Docker, Kubernetes) and model evaluation tools such as TensorBoard.