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Metro location and popular GenAI mid-senior role increase candidate competition.
Highly specialized LLM, multimodal, and MLOps expertise reduces cross-industry transferability.
Many mandatory technical skills, tools, certifications, and explicit years increase shortlisting rigor.
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Lead design, fine-tuning, and deployment of large language models and multimodal AI agents for enterprise automation and insights.
Develop and automate scalable AI pipelines for real-time inference, retraining, monitoring on cloud platforms.
Collaborate with cross-functional teams to translate use cases into operational AI systems ensuring performance, security, and compliance.
4+ years of experience in enterprise AI/ML projects involving LLMs, retrieval-augmented generation, and multimodal systems.
Extensive hands-on expertise in Python (3.8+), deep learning frameworks (PyTorch, TensorFlow), and cloud platforms (AWS, Azure, or GCP).
Bachelor’s or Master’s degree in Data Science, Computer Science, AI, or related technical field.
Hybrid work model requiring 3 days per week at client office.
Experienced in managing scalable AI systems in regulated enterprise environments with emphasis on secure, compliant deployments.
Proficient in MLOps practices including automated deployment, monitoring, retraining, and model lifecycle management with tools like Kubeflow or MLflow.
Effective at cross-team collaboration, guiding junior engineers, and translating business needs into AI solutions that enhance operational efficiency.