Senior Indirect Category Management Consultant
OhioHealth
Guide indirect category sourcing and supply chain improvements at OhioHealth. Lead contracting, supplier performance, and cost-reduction initiatives.
Open
EXL is a business consulting and services company that helps organizations use data, analytics, and technology to improve operations and make better decisions. Its work includes operations management, decision analytics, and digital transformation, with experience supporting clients in healthcare, insurance, finance, and logistics. EXL combines data science with practical business expertise to develop solutions tailored to each organization’s goals, operating model, and industry context.
GenAI Architect at EXL (Hybrid, India)
Shape enterprise generative AI solutions with large language models, from architecture and integration to deployment. Support responsible adoption through governance, security, and collaboration with business and engineering teams.
Description
- Create end-to-end generative AI solutions with LLMs such as GPT and Gemini.
- Set system architecture for APIs, pipelines, and integrations.
- Select suitable models, frameworks, and cloud platforms.
- Assess and choose foundation models and LLMs.
- Customize models through prompt engineering, RAG, and fine-tuning datasets.
- Connect generative AI solutions to enterprise CRM, ERP, and application systems.
- Deploy on Azure AI/OpenAI, AWS Bedrock, and Google Vertex AI.
- Protect data privacy and security while meeting compliance needs.
- Apply responsible AI methods, including bias reduction and explainability.
- Track model performance and accuracy.
- Partner with business stakeholders to identify generative AI opportunities.
- Advise engineering teams on implementation.
- Provide technical leadership for AI adoption.
Requirements
- Bachelor’s or master’s degree in computer science, AI, data science, or a related discipline.
- 5–12+ years of experience in software engineering, machine learning, or AI roles.
- Background working with enterprise-scale systems.
- Strong grounding in machine learning and deep learning.
- Strong understanding of large language models (LLMs).
- Knowledge of natural language processing (NLP).
- Experience with Python, APIs, and microservices.
- Experience with AI frameworks such as TensorFlow, PyTorch, and LangChain.
- Experience with Azure, AWS, or Google Cloud Platform.
- Experience in prompt engineering, RAG architectures, and LLM fine-tuning.
- Experience with vector databases such as Pinecone or FAISS.
- Strong problem-solving ability.
- Able to explain AI concepts to nontechnical teams.
- Strategic thinking.
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