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Tier-1 brand, metro location, mid-level role and broad GenAI skillset increases candidate competition.
GenAI engineering skills are broadly transferable, though advisory consulting context adds moderate domain bias.
Explicit 3+ years plus extensive mandatory GenAI, LLM, and DevOps tech stack requirements increase shortlist rigidity.
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Design and manage ML pipelines including experiment, model, and feature management plus model retraining at scale.
Develop APIs and leverage MLflow, SageMaker, Vertex AI, Azure AI for model inferencing and deployment.
Optimize and fine-tune large language models using GPU architectures and frameworks like DeepSpeed; implement LLM orchestration via Kubernetes and Docker.
Minimum 3+ years of relevant experience in Generative AI, LLMs, and ML pipeline development.
Mandatory skills: Gen AI, LLM, Hugging Face, Python, PyTorch/TensorFlow/Keras, Langchain, Langgraph, Docker, Kubernetes.
Degree required: Bachelor or Master of Engineering (BE/B.Tech/MEng); MBA or MCA also accepted.
Not explicitly mentioned: Notice period, location constraints, visa sponsorship details.
Experienced in deploying and managing large-scale machine learning and AI models in cloud environments (AWS, Azure, GCP).
Skilled in DevOps and MLOps practices specifically tailored for AI/LLM workflows including Kubernetes and container orchestration.
Capable of advanced model optimization and resource-efficient fine-tuning to improve latency and accuracy of LLM applications.