Position Overview Designing, implementing, and managing advanced generative AI solutions across Google Cloud Platform (GCP), Microsoft Azure, and Amazon Web Services (AWS). This role combines deep expertise in generative AI technologies, multi-cloud architecture, and modern software engineering practices to deliver scalable, secure, and innovative AI products. Key Responsibilities Design and implement generative AI workflows and agents using frameworks like LangChain, LangGraph, Google AI Development Kit (ADK), Azure AI Studio or AWS Bedrock. Integrate large language models (LLMs) such as Llama, Gemini, GPT, and Claude into enterprise systems and custom applications. Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases (ChromaDB, Pinecone, FAISS, Weaviate, Vertex AI Matching Engine). Cloud Platform Integration & Management Design and manage scalable cloud infrastructure, ensuring performance, cost efficiency, and compliance across platforms. Implement containerization and orchestration strategies using Docker and Kubernetes (GKE/EKS/AKS) for reliable deployment. Establish and enforce security frameworks using GCP IAM, Azure Identity, AWS IAM, and related tools for secure, compliant access. Utilize monitoring and logging solutions such as Google Cloud Operations Suite, Azure Monitor, and AWS CloudWatch. MLOps, DevOps & Governance Automate model deployment, versioning, and monitoring using MLOps/DevOps best practices and CI/CD pipelines. Implement prompt optimization, context management, and model performance tuning. Ensure adherence to data governance, privacy, PII handling, and AI ethics principles throughout the development lifecycle. Leadership & Collaboration Lead and mentor teams of AI engineers and developers, fostering a culture of collaboration and innovation. Conduct code reviews, enforce best practices, and guide junior engineers. Partner with product owners, data engineers, and business stakeholders to translate requirements into effective AI solutions. Communicate technical concepts, project status, and business impact to both technical and non-technical audiences, including executive leadership. Innovation & Continuous Improvement Drive research and innovation, staying current with advancements in generative AI, multimodal models, and AI agents. Contribute to internal capability building and development of reusable AI assets for the organization. Monitor AI model performance in production, manage risks, and drive continuous improvement initiatives. Required Skills & Experience 4+ years of IT experience, with 4 years in generative AI, LLMs, or related AI/ML technologies. Advanced proficiency in Python, with ability to build modular APIs, pipelines, and orchestration frameworks. Hands-on expertise in at least one generative AI framework: LangChain, LangGraph, Google ADK, Azure AI Studio or AWS Bedrock and understanding of others. Experience integrating LLMs via APIs (OpenAI, Gemini, Hugging Face, etc.). Strong knowledge of RAG, embeddings, and vector databases. Familiarity with PEFT, LoRA, or prompt fine-tuning approaches. Cloud expertise in at least one hyperscaler (GCP, Azure, or AWS), with working knowledge of: GCP: Vertex AI, Document AI, BigQuery, AlloyDB, Cloud Run, IAM Azure: OpenAI Service, Cognitive Search, Azure ML, Azure Functions, Azure Identity Education Bachelors or Master’s degree in Computer Science, AI, Data Science, or a related field. Nice to Have Experience with AI agents and multimodal AI models (text, image, audio).