Job Description: Role Overview: Design and deploy enterprise-grade AI solutions (LLMs, RAG, agents) by
selecting appropriate models, building data pipelines, and integrating them with cloud platforms
(AWS, Azure, GCP). Lead technical strategies, ensure scalability, manage AI security/
hallucinations, and bridge business needs with engineering teams.
Responsibilities: Key Responsibilities
System Design & Architecture: Architect end-to-end Generative AI systems, including
retrieval-augmented generation (RAG) and vector data systems.
Model Selection & Tuning: Evaluate and select cutting-edge commercial (e.g., GPT-4) and
open-source models, and fine-tune models for domain-specific use cases.
LLMOps & Pipelines: Establish LLMOps standards for model versioning, evaluation, prompt
management, and CI/CD, ensuring robust, production-grade AI.
Integration & Security: Integrate AI solutions with existing APIs, applications, and databases
while enforcing security, privacy, and guardrails to manage hallucinations and adversarial
attacks.
Strategic Leadership: Collaborate with stakeholders to map business challenges to AI
solutions and establish AI governance frameworks.
Qualifications: Bachelor’s / Master’s in Computer Science, AI, Data Science, or related field;
8–15 years in software engineering, ML, or AI roles. Experience with enterprise-level systems.
