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PwC brand, Bangalore metro, mid-level role and hot GenAI demand raise candidate competition.
Specialized LLM and ML engineering skills transfer across industries but require strong ML background.
Multiple mandatory LLM, ML, and DevOps tool proficiencies create stringent shortlisting filters.
Design and manage ML pipelines including experiment, model, feature management, and scalable inferencing APIs with tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Implement and optimize large language models (LLMs) involving distributed training, GPU architectures, and fine-tuning to improve latency, accuracy, and resource efficiency.
Apply DevOps and LLMOps skills using Kubernetes, Docker, and orchestration frameworks (e.g., Flowise, Langflow, Langgraph) for efficient deployment and management of AI models.
Minimum 3 years of work experience in related fields.
Mandatory skills: Generative AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Education required: BE/B.Tech or Master's in Engineering (M.E.) or equivalent relevant degree.
Work Experience Required: Minimum 3 years explicitly mentioned; notice period not explicitly mentioned.
Experienced in applying advanced machine learning engineering practices specifically in large language models and generative AI workflows.
Strong technical proficiency with ML Ops, DevOps, cloud AI services (AWS, Azure, GCP), and container-based deployment.
Comfortable working in advisory/data analytics context supporting client-facing solutions in AI and data engineering.