Sr. Associate- GenAI Engineering @ Synechron

We are seeking a highly skilled AI/ML Engineer with strong hands-on experience in building machine learning models, Large Language Models (LLMs), and agentic AI solutions. The ideal candidate should have deep expertise in Python, solid understanding of Java, and proven experience working on GCP (BigQuery). The role focuses on end-to-end model development, including designing, training, optimizing, and deploying ML/LLM-based systems into production.

• Design, develop, train, and test machine learning models, LLMs, and advanced AI solutions.
• Work on Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and contextual performance.
• Convert models into APIs or microservices for scalable production deployment.
• Optimize models for performance, latency, and cost-efficiency in production environments.

• Build and maintain reusable ML code, pipelines, and components using Python.
• Work with structured and unstructured data, implementing feature engineering and data preprocessing.
• Collaborate with cross-functional teams (Data Engineering, Cloud, Product) to integrate AI models into enterprise systems.

• Use Google Cloud Platform (GCP) services, specifically BigQuery, for data processing and model integration.
• Apply version control using Git and follow CI/CD best practices for ML workflows.
• Work with MLOps tools such as MLFlow for experiment tracking, reproducibility, and model lifecycle management.

• Python Expert level (mandatory)
• Java Working proficiency
• Strong knowledge of SQL and hands-on experience with NoSQL databases.
• Experience with deep learning and ML frameworks such as:
  • TensorFlow
  • PyTorch
  • Keras

• 3+ years of Experience building and tuning LLMs, including prompt engineering, fine-tuning, and embedding models.
• Hands-on exposure to RAG architectures.
• Understanding of agentic AI workflows and autonomous agents.

• Experience working on GCP, especially with BigQuery.
• Knowledge of Git and standard DevOps practices.
• Familiarity with MLOps tooling (MLFlow, model registries, experiment tracking).

• Exposure to vector databases (FAISS, Pinecone, Chroma).
• Experience with Docker/Kubernetes for model deployment.
• Understanding of microservices architecture.
• Knowledge of API development frameworks (FastAPI, Flask, Spring Boot).