GenAI GTM/Solutions Architect

EXL seeks a technically adept client-facing GenAI GTM/Solutions Architect , who will help customers operationalize generative AI agentic workflows.
We seek a leader who excels at translating cuttingedge Generative and Agentic AI capabilities into tangible business value for clients and the organization. The ideal candidate bridges the gap between advanced AI research and practical, robust, scalable, and ethical business applications, possessing both deep handson technical credibility and strategic consulting acumen to architect and implement AI solutions at an enterprise scale.
The opportunity to work on pioneering Generative and Agentic AI projects with a talented, global team.
A platform to influence AI strategy and adoption for major clients across various industries.
The chance to be a visible leader and make a significant impact on the future of AI application.
This highimpact, senior consulting role requires a unique blend of deep technical expertise in AI solutioning, GTM sales and selling AI solutions, Fortune500 client stakeholder management skills, and the ability to drive thought leadership. You will advise Insurance, Healthcare, and Banking clients, design cuttingedge AI solutions, lead complex implementations, and shape EXLs strategy and market presence in Generative and Agentic AI.
Youll collaborate closely with data science, technology, business development, and client teams to identify opportunities, architect robust solutions, and deliver transformative results. You will also lead clientfacing engagements, promote existing EXL AI solutions, design scalable GenAI solutions, and drive gotomarket success through demos and presentation materials.
Design and understand GenAI architecture across cloud platforms (Azure, AWS, GCP).
Lead client workshops to translate business challenges into AIdriven solutions, promoting existing EXL AI solutions such as finetuned LLMs, Agentic AI agents, Knowledge Graphs, advanced RAG pipelines, LLMOps and AI infrastructure.
Architect LLMbased systems using GPT4, Claude, Gemini, Llama2, and Mistral, including RAG pipelines, PEFT finetuning, and Agentic AI agent orchestration with LangGraph.
Ensure responsible AI practices, governance, and compliance with data privacy standards.
Selling AI solutions and partnering with sales teams to cocreate proposals and solution narratives.
Create GTM materials including pitch decks, demo scripts, and proofofconcept presentations.
Work closely with data scientists, engineers, and product managers to ensure seamless solution delivery.
Capture and share best practices across the AI architecture community.
Innovate AI architecture, adopting new methods to design and create advanced GenAI systems.
Engage senior client stakeholders to define project scope and prioritize use cases.
Design, architect, and oversee development and deployment of scalable, robust, cuttingedge AI systems.
Lead largescale, complex AI projects from conception to deployment, managing crossfunctional teams.
Mentor technical teams in best practices for AI development, deployment, MLOps/LLMOps, and agentic system design.
Communicate complex technical concepts and project progress to key stakeholders.
Ensure adherence to software engineering principles, Agile methodologies, and responsible AI practices.
Maintain deep knowledge of latest trends, research, tools, and technologies within Generative AI.

Bachelors degree in computer science, AI, machine learning, or a related quantitative field. 5+ years in AI/ML solution architecture, technical sales, or presales engineering, with proven experience with GenAI technologies, LLMs, and orchestration frameworks (LangChain, LangGraph, AutoGen).
~ Track record of architecting and deploying largescale AI projects, including complex GenAI and/or Agentic AI applications in enterprise or client settings.
~ Excellent communication skills, capable of explaining complex AI concepts to various stakeholders.
Project and program management skills with experience in Agile environments.
Experience with agentic AI workflows and autonomous agents.
Programming & Libraries : proficiency in Python and experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience developing applications leveraging stateoftheart LLMs (e.g., LangChain, LlamaIndex, Langfuse) and practical experience with agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph, agent planning, tool integration, multiagent collaboration).
AI Architecture : Deep understanding of AI/ML system architecture patterns, including microservices, eventdriven architectures, and patterns specific to RAG, Graph RAG, Agentic RAG, and multiagent systems.
Data : Knowledge of industry approaches to data engines and data labeling like Scale.ai and Mercor. Experience with auto datalabeling and synthetic data generation techniques.
Vector Databases & Embeddings : Expertise in working with various embedding models and vector databases (e.g., Advanced AI Concepts : Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, autonomous agent design, and evaluation methodologies for complex AI systems.
Software Engineering : Strong foundation in software engineering principles for building scalable, maintainable, and productionready AI systems.
Cloud Platforms : Strong working knowledge and practical deployment experience on at least one major cloud platform (AWS, Azure, GCP), including their AI/ML services.
Expertise in designing and implementing robust MLOps/LLMOps pipelines for automated testing, CI/CD, monitoring, and governance of complex AI models and applications.