Generative AI Engineer

AVP – Enterprise GenAI Architect (Cross-Functional)

Experience: 8–15 Years

Role Overview

Design and deploy enterprise-grade Generative AI solutions, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents. The role involves selecting appropriate AI models, building scalable data pipelines, integrating AI capabilities with cloud platforms (AWS, Azure, or GCP), and leading enterprise AI architecture initiatives.

Drive technical strategy, ensure scalability and reliability, implement AI security and governance, and collaborate with business and engineering teams to deliver impactful AI solutions.

Key Responsibilities

System Design & Architecture

• Architect end-to-end Generative AI solutions using LLMs, RAG, AI agents, and vector databases.
• Design scalable, secure, and production-ready AI application architectures.
• Develop reusable AI frameworks and reference architectures for enterprise use cases.

Model Selection & Optimization

• Evaluate and select commercial and open-source LLMs based on business and technical requirements.
• Fine-tune and optimize models for domain-specific applications.
• Define evaluation criteria for model performance, latency, accuracy, and cost.

LLMOps & AI Engineering

• Establish LLMOps best practices for prompt management, model versioning, experimentation, evaluation, deployment, and monitoring.
• Build automated CI/CD pipelines for AI applications.
• Define standards for testing, observability, and production monitoring of AI systems.

AI Integration & Security

• Integrate AI solutions with enterprise applications, APIs, databases, and cloud services.
• Implement security controls, governance policies, guardrails, and responsible AI practices.
• Mitigate hallucinations, prompt injection attacks, data leakage, and adversarial risks.

Cloud & Data Architecture

• Design AI solutions leveraging AWS, Microsoft Azure, or Google Cloud Platform services.
• Implement scalable vector search using modern vector databases.
• Build efficient document ingestion, embedding, indexing, and retrieval pipelines.

Strategic Leadership

• Partner with business stakeholders to identify AI opportunities and translate business challenges into technical solutions.
• Define enterprise AI architecture standards and governance frameworks.
• Mentor engineering teams and provide technical leadership across AI initiatives.
• Stay current with emerging AI technologies and recommend adoption strategies.

Required Skills & Qualifications

Technical Skills

• Strong expertise in Python and modern software engineering practices.
• Deep understanding of NLP, Generative AI, LLMs, and deep learning frameworks such as PyTorch or TensorFlow.
• Experience with AI orchestration frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies.
• Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) solutions.
• Expertise in prompt engineering, embeddings, semantic search, vector databases, and LLM fine-tuning.

Cloud & Data Platforms

• Extensive experience with AI services on AWS, Azure, or Google Cloud Platform.
• Knowledge of vector databases such as Pinecone, Milvus, Chroma, FAISS, or Weaviate.
• Experience building scalable data pipelines and integrating enterprise data sources.

Enterprise Architecture

• Experience designing distributed, scalable, and secure enterprise applications.
• Strong understanding of API design, microservices, authentication, and cloud-native architectures.
• Familiarity with AI governance, compliance, monitoring, and observability.

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to explain complex AI concepts to technical and non-technical audiences.
• Strategic thinking with a focus on business outcomes.
• Experience leading cross-functional teams and enterprise AI initiatives.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, or a related field.
• 8–15 years of experience in Software Engineering, Machine Learning, Artificial Intelligence, Data Engineering, or related domains.
• Proven experience delivering enterprise-scale AI solutions from design through production deployment.