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Tier-1 brand, Bangalore metro location, and mid-level experience amplify competition despite niche GenAI skills.
GenAI and ML engineering skills are broadly transferable across industries with low domain lock-in.
Explicit 4–7 years plus mandatory LLM, PyTorch, cloud, LangChain, and MLOps tech stack requirements.
Design, develop, and deploy scalable Generative AI solutions using large language models (LLMs) and transformer architectures.
Build and optimize AI model pipelines and orchestrate workflows with frameworks like LangChain, ensuring integration with enterprise systems and cloud platforms (Azure/AWS/GCP).
Collaborate with ML engineers and data teams to productionize, evaluate, and improve AI models ensuring robustness, scalability, and compliance.
4 to 7 years of work experience.
Bachelor's degree in Technology (B.E/B.Tech) or Master's in Technology/MCA.
Proficiency in Python, PyTorch, Hugging Face Transformers, and large language models.
Experience with cloud platforms (Azure, AWS, GCP), orchestration frameworks (LangChain or similar), REST API development (FastAPI, Flask), ML pipeline tools (MLflow, Weights & Biases), and CI/CD for ML (e.g., Azure ML, SageMaker).
Experienced in applying generative AI and transformer-based models in production environments across cloud platforms.
Skilled in integrating AI capabilities into enterprise applications with an emphasis on pipeline robustness and scalability.
Demonstrated ability to collaborate with cross-functional teams (data engineers, MLOps) to deliver scalable AI solutions operating at the intersection of AI research and enterprise deployment.