10 hours ago
Responsibilities
- Architect and implement enterprise-scale Generative AI solutions using LLMs, foundation models, and agentic AI frameworks.
- Create reusable AI patterns, accelerators, reference architectures, components, tools, agents, and workflow templates.
- Translate business requirements into scalable AI workflows, prototypes, MVPs, and production-ready applications.
- Develop prompt engineering frameworks, prompt versioning and lifecycle management, testing strategies, governance, and guardrails.
- Build orchestration pipelines and multi-agent workflows using frameworks such as LangGraph, LangChain, CrewAI, and Semantic Kernel.
- Design cloud-native GenAI architectures across AWS, GCP, and Snowflake, including RAG systems with vector databases, embeddings, and semantic search.
- Establish evaluation and benchmarking frameworks covering accuracy, relevance, latency, safety, business impact, and model or workflow performance.
- Define KPIs, observability, and monitoring strategies while optimizing retrieval, inference performance, reliability, and infrastructure costs.
- Partner with business stakeholders to identify high-value AI opportunities and drive adoption of emerging GenAI technologies.
Requirements
- Bachelor's or master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
- 8+ years of experience in software engineering, machine learning, data engineering, or AI solution development.
- 4+ years of hands-on experience with Generative AI, LLMs, and foundation models.
- Strong experience designing enterprise GenAI solutions using advanced LLM architectures and prompt frameworks.
- Expertise in scalable AI workflows, orchestration pipelines, reusable AI components, prompt governance, guardrails, and controlled LLM outputs.
- Experience translating business requirements into production-ready AI solutions and prototypes using synthetic data.
- Strong knowledge of AI evaluation frameworks, benchmarking methodologies, optimization techniques, vector databases, embeddings, semantic search, and RAG architectures.
- Strong proficiency in Python and modern AI/ML development frameworks.
Tech Stack
Categories
About EXL
EXL provides data analytics, AI, and digital operations services for enterprises in insurance, healthcare, banking, and other regulated industries. It delivers consulting, managed services, and technology platforms to improve decision-making, risk management, and operational efficiency. Founded in 1999 and headquartered in New York, EXL is a public company listed on the NASDAQ (ticker: EXLS).