Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Niche LLM specialization and seniority reduce applicant density.
Highly specialized LLM, MLOps, and AI architecture skills limit cross-industry transferability.
Extensive mandatory LLM, MLOps, cloud, security, and deployment requirements drive high filtering.
Lead design, development, and deployment of AI/ML and Generative AI solutions including LLM-powered applications and AI agents.
Define AI technical strategy, architectural standards, and engineering practices; lead integration of AI systems with cloud infrastructure and enterprise applications.
Establish evaluation frameworks, AI security standards, and reliability mechanisms; mentor AI/ML engineers and drive AI initiatives from proof-of-concept to production with measurable business impact.
Strong proficiency in Python and AI/ML frameworks (PyTorch, TensorFlow, LangChain, LlamaIndex).
Experience with Generative AI, LLM platforms (OpenAI, Anthropic, Azure OpenAI, Hugging Face), RAG, embeddings, vector databases, agentic workflows, AI orchestration and human-in-the-loop systems.
Hands-on with cloud AI services (AWS, Azure, GCP), microservices, REST APIs, distributed systems, Docker, Kubernetes, CI/CD, MLOps/LLMOps.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in leading AI/ML architecture strategy and operationalizing Generative AI solutions in production environments at scale.
Comfortable working across multiple AI technologies including LLMs, vector search, prompt/context engineering, and AI security/governance.
Skilled at collaborating with product and engineering teams to translate business requirements into scalable, measurable AI systems and mentoring technical teams.