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Protocol Intelligence
Data-driven signals on your job's competitivenessHigh due to Tier‑1 brand, Bangalore metro, and popular mid‑level GenAI role attracting many applicants.
High: niche GenAI/LLMOps, GPU and distributed training expertise reduces cross‑industry transferability.
High: explicit 3+ years plus mandatory LLM/GenAI, PyTorch, Langchain, Docker and Kubernetes skills.
Job Description
Structured overview of role & requirementsAbout This Role
Design and manage ML pipelines including experiment, model, and feature management plus API design for scalable model inferencing.
Specialize in LLM serving with deep knowledge of GPU architectures and expertise in distributed training and serving large language models using frameworks like DeepSpeed and vLLM.
Optimize and fine-tune models to improve latency and accuracy while reducing training and resource requirements; apply DevOps and LLMOps best practices using Kubernetes, Docker, and orchestration frameworks such as Flowise and Langflow.
Minimum Requirements
Minimum 3+ years of relevant experience in Gen AI, LLM, Huggingface, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, and Kubernetes.
Educational qualification: Bachelor or Master in Engineering (BE/B.Tech/ME) or MBA/MCA.
Proficiency in Python, SQL, and JavaScript is mandatory.
DevOps knowledge including Kubernetes, Docker; experience with cloud platforms AWS/Azure/GCP; Cloud certifications are a plus but not mandatory.
Ideal Candidate Profile
Experienced in designing and deploying ML pipelines and APIs for production-level Gen AI applications focusing on large language models.
Strong hands-on expertise with LLM Ops tools such as MLflow, Langchain, SageMaker, and Azure AI demonstrating operational maturity in model lifecycle.
Capable of integrating complex distributed GPU training and optimization, balancing model performance with resource efficiency in cloud and containerized environments.