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PwC brand and Bangalore metro increase competition, but niche GenAI specialization reduces applicant density.
GenAI engineering skills are transferable across industries, though consulting and enterprise-data experience increases specificity.
Numerous mandatory technical filters (Python, LangChain, vector DBs, cloud AI, LLMOps) create a high shortlisting bar.
Develop and deploy enterprise-grade Generative AI applications and APIs with emphasis on optimizing RAG pipelines and integrating LLMs using major cloud AI platforms.
Build multi-agent systems and autonomous workflows to automate complex, multi-step business processes using orchestration frameworks.
Implement responsible AI guardrails, monitor LLM performance metrics, and rapidly prototype Proof of Concepts for internal and client demonstrations.
4-7 years of software development experience with strong computer science fundamentals.
Expertise in Python programming and API development using frameworks like FastAPI, Flask, or Django.
Hands-on experience with at least one major cloud AI ecosystem: GCP (Vertex AI), Azure (Azure OpenAI), or AWS (Amazon Bedrock).
Bachelor’s degree in Technology (BE/BTech) or related fields such as MTech, MCA, or MBA (full-time).
Experienced in advanced Generative AI technologies including LangChain, vector databases, prompt engineering, and embeddings.
Capable of handling containerized deployments and CI/CD pipelines with familiarity in Docker, Kubernetes basics, and LLM monitoring tools.
Skilled in integrating and orchestrating multi-agent AI workflows with a focus on practical enterprise AI solution delivery.