• Design, develop, and deploy Generative AI solutions leveraging Large Language Models (LLMs) and multimodal AI technologies. • Build and maintain scalable AI applications using cloud platforms such as Azure, AWS, or GCP. • Develop and optimize Retrieval-Augmented Generation (RAG) architectures, vector databases, and knowledge retrieval systems. • Fine-tune, evaluate, and monitor foundation models to improve performance, accuracy, and reliability. • Implement prompt engineering strategies and AI orchestration frameworks to support business use cases. • Collaborate with software engineering, data science, DevOps, and security teams to integrate AI solutions into production environments. • Develop APIs, microservices, and AI-powered applications following software engineering best practices. • Ensure compliance with AI governance, security, privacy, and responsible AI standards. • Monitor AI workloads, model performance, and operational costs, recommending continuous improvements. • Stay current with emerging Generative AI technologies, frameworks, and industry trends.

• Design and deploy AI platforms that integrate with infrastructure tools • Develop AI-powered workflows to automate operational tasks • Build AI-driven automation for incident response and operational workflows • Implement AI-powered monitoring and anomaly detection capabilities • Create intelligent operational dashboards with actionable insights • Ensure AI platforms operate reliably in production environments • Develop AI solutions for cost optimization and predictive capacity planning

• Add AI functionality into existing Python, Node.js, and Ruby codebases • Build LLM-powered features: chat, summaries, classification, smart search, document Q&A • Design lightweight RAG pipelines using embeddings and vector search • Work with vector DBs (pgvector, Pinecone, Qdrant) • Implement safe, reliable LLM endpoints (OpenAI, Anthropic, Azure) • Work directly with clients to shape AI features and reduce manual effort • Advise clients when NOT to use AI and navigate trade-offs around latency, accuracy, and cost

• Design, build, and maintain scalable ML infrastructure and pipelines supporting model training, deployment, monitoring, governance, and lifecycle management. • Develop and optimize CI/CD pipelines for machine learning and AI workloads across development, staging, and production environments. • Build reusable ML platform capabilities including feature stores, model registries, experimentation frameworks, artifact management, and deployment automation. • Implement scalable orchestration and workflow solutions for batch and real-time ML inference workloads. • Create robust monitoring systems to measure model performance, detect model drift, monitor data quality, and ensure production reliability. • Develop automation tools and self-service capabilities to improve the efficiency, scalability, and reliability of MLOps processes. • Collaborate with Data Scientists and Software Engineers to streamline the ML lifecycle from experimentation through enterprise production deployment. • Apply software engineering best practices to AI/ML systems including testing, observability, resiliency, security, versioning, and infrastructure-as-code. • Identify gaps and improvement opportunities within the organization’s ML platform ecosystem and architect scalable solutions to address them. • Support enterprise AI governance, compliance, auditability, and model risk management requirements. • Ensure platform scalability, reliability, security, and operational excellence across AI/ML systems. • Lead the architecture, design, and deployment of enterprise Generative AI solutions leveraging LLMs, foundation models, and agentic AI systems. • Design and implement Retrieval-Augmented Generation (RAG) pipelines using vector databases, embeddings, semantic search, reranking, and retrieval optimization strategies. • Build scalable LLM orchestration frameworks using technologies such as LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks. • Develop advanced prompt engineering strategies, prompt chaining, context management, and agent workflows to improve LLM accuracy and reliability. • Evaluate and implement fine-tuning, parameter-efficient tuning, and prompt-based optimization approaches for domain-specific use cases. • Build AI evaluation and benchmarking frameworks to measure hallucination rates, response quality, grounding accuracy, toxicity, bias, latency, and business performance metrics. • Implement AI safety guardrails, governance controls, content filtering, and responsible AI practices for enterprise healthcare environments. • Design scalable GenAI APIs and microservices supporting high-throughput enterprise AI applications. • Optimize GenAI systems for cost, latency, throughput, and inference performance across cloud and hybrid environments. • Integrate enterprise data sources, healthcare systems, and knowledge repositories into secure GenAI workflows. • Research and evaluate emerging GenAI technologies, open-source frameworks, and foundation models to drive innovation and continuous improvement. • Develop architecture diagrams, technical roadmaps, implementation strategies, and executive-level documentation for enterprise AI initiatives. • Collaborate with cybersecurity, compliance, and infrastructure teams to ensure secure and compliant deployment of GenAI solutions involving PHI and sensitive healthcare data. • Contribute to the development of AI platform standards, reusable GenAI accelerators, templates, and engineering best practices.