Tredence Analytics Solutions - Senior Machine Learning Engineer

Job Description : We are looking for a Machine Learning Engineer with expertise in MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations) to design, deploy, and maintain scalable AI/ML systems. You will work on automating ML workflows, optimizing model deployment, and managing large-scale AI applications, including LLMs (Large Language Models), ensuring they run efficiently in production.Key Responsibilities : - Design and implement end-to-end MLOps pipelines for training, validation, deployment, monitoring, and retraining of ML models.- Optimize and fine-tune large language models (LLMs) for various applications, ensuring performance and efficiency.- Develop CI/CD pipelines for ML models to automate deployment and monitoring in production.- Monitor model performance, detect drift, and implement automated retraining mechanisms.- Work with cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes) for scalable deployments.- Implement best practices in data engineering, feature stores, and model versioning.- Collaborate with data scientists, engineers, and product teams to integrate ML models into production applications.- Ensure compliance with security, privacy, and ethical AI standards in ML deployments.- Optimize inference performance and cost of LLMs using quantization, pruning, and distillation techniques.- Deploy LLM-based APIs and services, integrating them with real-time and batch processing pipelines.Key Requirements : Technical Skills : - Strong programming skills in Python, with experience in ML frameworks (TensorFlow, PyTorch, Hugging Face, JAX).- Experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker, Airflow).- Deep understanding of LLM architectures, prompt engineering, and fine-tuning.- Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools.- Proficiency in cloud services (AWS/GCP/Azure) for ML model training and deployment.- Experience with monitoring ML models (Prometheus, Grafana, Evidently AI).- Knowledge of feature stores (Feast, Tecton) and data pipelines (Kafka, Apache Beam).- Strong background in distributed computing (Spark, Ray, Dask).Soft Skills : - Strong problem-solving and debugging skills.- Ability to work in cross-functional teams and communicate complex ML concepts to stakeholders.- Passion for staying updated with the latest ML and LLM research & technologies.Preferred Qualifications : - Experience with LLM fine-tuning, Reinforcement Learning with Human Feedback (RLHF), or LoRA/PEFT techniques.- Knowledge of vector databases (FAISS, Pinecone, Weaviate) for retrieval-augmented generation (RAG).- Familiarity with LangChain, LlamaIndex, and other LLMOps-specific frameworks.- Experience deploying LLMs in production (ChatGPT, LLaMA, Falcon, Mistral, Claude, etc.). (ref:hirist.tech)