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Tier-1 PwC brand, Bangalore metro location, and mid-level 3-5 year role produce high competition.
GenAI model engineering and cloud deployment skills are transferable but need ML-specific experience.
Explicit 3–5 years plus many mandatory GenAI, PyTorch, cloud, and ML tooling requirements.
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Design, build, and deploy scalable generative AI solutions using large language models (LLMs) and transformer architectures.
Collaborate with data engineers and MLOps teams to productionize GenAI models on cloud platforms (Azure, AWS, GCP) ensuring robustness, scalability, and compliance.
Integrate GenAI capabilities into enterprise systems via APIs and custom interfaces, optimize prompt engineering, and iterate on model performance improvement.
3 to 5 years of work experience in generative AI solution development.
Proficient in Python, PyTorch, Hugging Face Transformers, and cloud platforms (Azure, AWS, or GCP).
Experience with orchestration frameworks like LangChain, and ML pipeline tools such as MLflow or Weights & Biases.
Bachelor's or Master's degree in engineering or computer applications required (B.E/B.Tech/M.Tech/MCA); MBA listed but likely an error or not mandatory.
Strong expertise in generative AI with hands-on experience deploying and fine-tuning foundation models in production cloud environments.
Experience working with orchestration and CI/CD tools for ML pipelines indicating operationalizing AI solutions at scale.
Capable of integrating advanced GenAI functionalities into enterprise applications and collaborating cross-functionally with engineering and MLOps teams.