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Tier-1 brand, mid-level experience band, and Bangalore location increase applicant density despite niche GenAI requirements.
Core GenAI and ML engineering skills are transferable, but enterprise integration and domain specifics limit portability.
Explicit 4–7 years plus mandatory GenAI, PyTorch, cloud, and LangChain skills enforce strict filtering.
Design, develop, and deploy scalable Generative AI solutions using large language models and transformer architectures.
Implement and optimize model workflows, prompt engineering, and AI model pipelines using Python, PyTorch, Hugging Face, and orchestration frameworks like LangChain.
Collaborate with teams to productionize GenAI models on cloud platforms (Azure, AWS, GCP) and integrate AI capabilities via APIs into enterprise systems.
4 to 7 years of work experience in relevant fields.
Proficiency in Generative AI (LLMs, Transformers), Python, PyTorch, Hugging Face Transformers.
Experience deploying AI solutions on cloud platforms: Azure, AWS, or GCP.
Hands-on experience with orchestration frameworks (LangChain or similar), REST API development (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), and CI/CD for ML.
Experienced engineer with a strong background in GenAI, foundational models, and cloud-based AI deployment workflows.
Skilled in integrating and productionizing AI models collaboratively with data engineering and MLOps teams.
Able to design and fine-tune AI solutions focusing on robustness, scalability, and domain-specific customization.