We are seeking a highly skilled Senior Generative AI Engineer Lead to drive the design, development, and deployment of enterprise-grade Generative AI solutions. The ideal candidate will have deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks, cloud-native AI architectures, and model evaluation methodologies.
This role will lead the end-to-end lifecycle of GenAI initiatives, from translating business requirements into AI-powered prototypes to delivering scalable, production-ready solutions across AWS, GCP, and Snowflake ecosystems. The candidate will also establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization.
ResponsibilitiesGenerative AI Solution Design
- Architect and implement enterprise-scale GenAI solutions using LLMs, foundation models, and agentic AI frameworks.
- Design reusable AI patterns, accelerators, and reference architectures to enable rapid solution development.
- Translate business problems into scalable AI workflows and production-ready proof-of-concepts.
- Drive AI platform modernization through adoption of emerging GenAI technologies and best practices.
Prompt Engineering & LLM Governance
- Develop and maintain sophisticated prompt engineering frameworks for controlled and reliable LLM outputs.
- Implement prompt versioning, prompt lifecycle management, and testing strategies.
- Design AI guardrails to mitigate hallucinations, bias, security risks, and compliance concerns.
- Establish best practices for prompt optimization, response consistency, and output quality management.
AI Workflow Orchestration & Automation
- Design and build scalable orchestration pipelines using frameworks such as LangGraph, LangChain, CrewAI, Semantic Kernel, or equivalent.
- Develop reusable AI components, tools, agents, and workflow templates for enterprise adoption.
- Implement multi-agent systems and autonomous workflows to support complex business use cases.
Prototyping & Business Enablement
- Partner with business stakeholders to identify high-value AI opportunities.
- Rapidly develop AI prototypes and MVP solutions using synthetic and enterprise datasets.
- Convert prototypes into production-ready applications adhering to scalability, security, and reliability standards.
Cloud & Data Engineering
- Build scalable GenAI architectures across AWS, GCP, and Snowflake platforms.
- Leverage cloud-native AI services including Amazon Bedrock, SageMaker, Vertex AI, Snowflake Cortex AI, and related ecosystems.
- Design robust RAG (Retrieval-Augmented Generation) architectures incorporating vector databases, embeddings, and semantic search.
- Optimize model deployment, inference performance, and infrastructure cost efficiency.
Evaluation & Performance Optimization
- Establish AI evaluation frameworks to measure accuracy, relevance, latency, safety, and business impact.
- Develop benchmarking methodologies for comparing prompts, models, and workflows.
- Define KPIs, observability frameworks, and monitoring strategies for GenAI applications.
- Continuously improve model performance through prompt tuning, retrieval optimization, and workflow enhancements.
- 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 utilizing advanced LLM architectures and prompt frameworks.
- Expertise in building scalable AI workflows, orchestration pipelines, and reusable AI components.
- Proven ability to translate business requirements into production-ready AI solutions and prototypes using synthetic data.
- Deep understanding of prompt versioning, prompt governance, guardrails, and controlled LLM outputs.
- Strong knowledge of AI evaluation frameworks, benchmarking methodologies, and optimization techniques.
- Experience with vector databases, embeddings, semantic search, and RAG architectures.
- Strong proficiency in Python and modern AI/ML development frameworks.
Skills Required
- 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
- Experience designing enterprise Generative AI solutions using advanced LLM architectures and prompt frameworks
- Expertise building scalable AI workflows, orchestration pipelines, and reusable AI components
- Ability to translate business requirements into production-ready AI solutions and prototypes using synthetic data
- Knowledge of prompt versioning, prompt governance, guardrails, and controlled LLM outputs
- Knowledge of AI evaluation frameworks, benchmarking methodologies, and optimization techniques
- Experience with vector databases, embeddings, semantic search, and RAG architectures
- Strong proficiency in Python and modern AI/ML development frameworks
EXL Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.
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Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
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Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
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Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.
EXL Insights
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
