Gen AI - Engineering Lead

EXL

United States

Who this role is best for

Aimed at senior AI engineers with enterprise solution experience who can lead GenAI initiatives and govern LLM outputs.

Best fit for

Senior AI engineers with 8+ years of experience in building enterprise-grade AI solutions
— “8+ years of experience in software engineering, machine learning, data engineering, or AI solution development.”
Candidates who have led end-to-end GenAI projects and translated business needs into technical solutions
— “Translate business requirements into AI-powered prototypes to delivering scalable, production-ready solutions.”
Individuals with a strong background in prompt governance and model evaluation methodologies
— “Establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization.”

Things to consider

This role demands a high level of technical depth in LLMs and orchestration frameworks, not just surface-level familiarity
— “deep expertise in Large Language Models (LLMs), prompt engineering, AI orchestration frameworks”
The position requires working with cloud platforms like AWS, GCP, and Snowflake, which may involve specific infrastructure commitments
— “Build scalable GenAI architectures across AWS, GCP, and Snowflake platforms.”

How to stand out

Highlight experience in designing reusable AI patterns and reference architectures in your resume
— “Design reusable AI patterns, accelerators, and reference architectures”
Emphasize your track record in converting prototypes into production-ready applications
— “Convert prototypes into production-ready applications adhering to scalability, security, and reliability standards.”
Demonstrate your ability to implement prompt versioning and governance systems in interviews
— “Develop and maintain sophisticated prompt engineering frameworks for controlled and reliable LLM outputs.”
Showcase your hands-on experience with orchestration frameworks such as LangChain or CrewAI
— “Design and build scalable orchestration pipelines using frameworks such as LangGraph, LangChain, CrewAI”
Quantify your impact using AI evaluation frameworks and benchmarking methodologies
— “Establish AI evaluation frameworks to measure accuracy, relevance, latency, safety, and business impact.”

Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Design and implement enterprise-scale GenAI solutions

Typical background

8+ years of experience in software engineering, machine learning, data engineering, or AI solution development

Skills & requirements

Required

Generative AILarge Language Models (llms)Prompt EngineeringAI Orchestration FrameworksCloud-native AI ArchitecturesModel Evaluation Methodologies

Preferred

LanggraphLangchainCrewaiSemantic Kernel

Stack & domain

Large Language Models (llms)Prompt EngineeringAI Orchestration FrameworksCloud-native AI ArchitecturesModel Evaluation MethodologiesLanggraphLangchainCrewaiSemantic KernelRAG (retrieval-augmented Generation)Vector DatabasesEmbeddingsSemantic SearchPythonLeadershipCommunicationFinanceHealthcare

About the role

Original posting from EXL

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. The candidate will also establish engineering best practices around prompt governance, model guardrails, benchmarking, and performance optimization.
Generative 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.

Source: EXL careers