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Tier-1 employer, mid-level (3+ yrs), metro Bangalore increase competition despite niche LLM skills.
Core LLM engineering skills are transferable across industries but advisory context adds moderate domain preference.
Many mandatory LLM/ML, DevOps and cloud skills plus minimum 3+ years requirement.
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Design and manage ML pipelines including experiment, model, and feature management using tools like MLflow, SageMaker, Vertex AI, and Azure AI.
Lead deployment and scaling of large language models with expertise in GPU architectures, distributed training (DeepSpeed), and LLM serving frameworks (vLLM).
Apply fine-tuning and optimization techniques to improve model latency, accuracy, and resource efficiency, alongside DevOps and LLMOps practices involving Kubernetes, Docker, and orchestration frameworks (Flowise, Langflow, Langgraph).
3+ years of work experience in relevant AI/ML engineering roles.
Bachelor's or Master's degree in Engineering (BE/B.Tech/Master of Engineering) or related technical field; MBA or MCA also accepted.
Mandatory skills: Generative AI, LLMs, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Proficient with cloud platforms AWS, Azure, or GCP; DevOps tools such as Kubernetes and Docker are essential.
Experienced in building and operationalizing production-grade ML pipelines for large language models using advanced AI platforms and orchestration frameworks.
Strong in distributed training and fine-tuning of LLMs with efficient resource management and performance optimization.
Comfortable working in cloud-based environments with hands-on DevOps skills and familiar with modern LLMOps frameworks and tools, suited for advisory consulting roles.