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Agentic/AI lead/architect with Claude/code/LLM skills1
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Job Info
- Job Identification13441
- Posting Date05/27/2026, 08:22 AM
- Job RoleAI architecture-AI architect
- Experience (In Years)9-12
Job Description
Key Responsibilities
GenAI & Agentic AI Architecture
-
Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including:
-
Single-agent and multi-agent systems
-
Tool-calling and function orchestration
-
Memory, planning, and execution layers
-
Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost–latency trade-offs.
-
Design secure-by-default GenAI systems incorporating:
-
Guardrails and policy enforcement
-
Data privacy, PII handling, and prompt safety
-
Controlled tool execution in regulated environments
RAG, Knowledge & Data Systems
-
Architect large-scale RAG solutions, covering:
-
Data ingestion and curation pipelines
-
Chunking and embedding strategies
-
Vector databases and hybrid search
-
Evaluation and feedback loops
-
Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.
Platform & Engineering Excellence
-
Drive production readiness of GenAI systems:
-
API-first design (FastAPI / REST / event-driven)
-
CI/CD for LLM workflows
-
Monitoring, evaluation, and cost tracking
-
Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
-
Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.
Leadership & Stakeholder Engagement
- Act as a technical authority for GenAI across delivery teams and client engagements.
- Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
- Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation.
Mandatory Skills & Experience
12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems, and strong prior background in Data Engineering or Data Science (mandatory).
Generative AI / LLM Expertise
- Deep hands-on experience with:
- Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus)
- Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.)
- Strong command over:
- Prompt engineering, prompt orchestration, and agent workflows
- Tool/function calling, planning–execution loops
- LLM and RAG evaluation techniques (precision, grounding, faithfulness)
Agentic & RAG Architecture
- Proven experience designing:
- Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups)
- RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.)
- Strong understanding of hallucination mitigation, guardrails, and safety frameworks.
Core Engineering & Platform Skills
- Expert-level Python engineering (production-grade systems).
- Strong experience with cloud-native AI solutions on Azure, AWS, or GCP.
- API design, microservices, and event-driven architectures.
Mandatory Prior Background
-
Data Engineering or Data Science experience is non-negotiable, including:
-
Data pipelines / ETL / ELT / orchestration
-
ML or NLP model lifecycle
-
Analytics platforms or data product engineering
Good-to-Have / Preferred
- Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning).
- Experience with MLOps / LLMOps platforms and observability stacks.
- Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS).
- Exposure to enterprise AI governance frameworks.
Responsibilities
Key Responsibilities
GenAI & Agentic AI Architecture
-
Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including:
-
Single-agent and multi-agent systems
-
Tool-calling and function orchestration
-
Memory, planning, and execution layers
-
Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost–latency trade-offs.
-
Design secure-by-default GenAI systems incorporating:
-
Guardrails and policy enforcement
-
Data privacy, PII handling, and prompt safety
-
Controlled tool execution in regulated environments
RAG, Knowledge & Data Systems
-
Architect large-scale RAG solutions, covering:
-
Data ingestion and curation pipelines
-
Chunking and embedding strategies
-
Vector databases and hybrid search
-
Evaluation and feedback loops
-
Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.
Platform & Engineering Excellence
-
Drive production readiness of GenAI systems:
-
API-first design (FastAPI / REST / event-driven)
-
CI/CD for LLM workflows
-
Monitoring, evaluation, and cost tracking
-
Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
-
Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.
Leadership & Stakeholder Engagement
- Act as a technical authority for GenAI across delivery teams and client engagements.
- Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
- Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation.
Mandatory Skills & Experience
12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems, and strong prior background in Data Engineering or Data Science (mandatory).
Generative AI / LLM Expertise
- Deep hands-on experience with:
- Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus)
- Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.)
- Strong command over:
- Prompt engineering, prompt orchestration, and agent workflows
- Tool/function calling, planning–execution loops
- LLM and RAG evaluation techniques (precision, grounding, faithfulness)
Agentic & RAG Architecture
- Proven experience designing:
- Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups)
- RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.)
- Strong understanding of hallucination mitigation, guardrails, and safety frameworks.
Core Engineering & Platform Skills
- Expert-level Python engineering (production-grade systems).
- Strong experience with cloud-native AI solutions on Azure, AWS, or GCP.
- API design, microservices, and event-driven architectures.
Mandatory Prior Background
-
Data Engineering or Data Science experience is non-negotiable, including:
-
Data pipelines / ETL / ELT / orchestration
-
ML or NLP model lifecycle
-
Analytics platforms or data product engineering
Good-to-Have / Preferred
- Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning).
- Experience with MLOps / LLMOps platforms and observability stacks.
- Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS).
- Exposure to enterprise AI governance frameworks.
Qualifications
Key Responsibilities
GenAI & Agentic AI Architecture
-
Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including:
-
Single-agent and multi-agent systems
-
Tool-calling and function orchestration
-
Memory, planning, and execution layers
-
Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost–latency trade-offs.
-
Design secure-by-default GenAI systems incorporating:
-
Guardrails and policy enforcement
-
Data privacy, PII handling, and prompt safety
-
Controlled tool execution in regulated environments
RAG, Knowledge & Data Systems
-
Architect large-scale RAG solutions, covering:
-
Data ingestion and curation pipelines
-
Chunking and embedding strategies
-
Vector databases and hybrid search
-
Evaluation and feedback loops
-
Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.
Platform & Engineering Excellence
-
Drive production readiness of GenAI systems:
-
API-first design (FastAPI / REST / event-driven)
-
CI/CD for LLM workflows
-
Monitoring, evaluation, and cost tracking
-
Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
-
Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.
Leadership & Stakeholder Engagement
- Act as a technical authority for GenAI across delivery teams and client engagements.
- Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
- Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation.
Mandatory Skills & Experience
12+ years of total experience with deep hands-on expertise in Generative AI / LLM-based systems, and strong prior background in Data Engineering or Data Science (mandatory).
Generative AI / LLM Expertise
- Deep hands-on experience with:
- Claude / Anthropic ecosystem (including Claude Code exposure is a strong plus)
- Other enterprise LLMs (OpenAI, Mistral, LLaMA, etc.)
- Strong command over:
- Prompt engineering, prompt orchestration, and agent workflows
- Tool/function calling, planning–execution loops
- LLM and RAG evaluation techniques (precision, grounding, faithfulness)
Agentic & RAG Architecture
- Proven experience designing:
- Agentic AI systems (ReAct, Plan-and-Execute, multi-agent setups)
- RAG architectures using vector databases (FAISS, Pinecone, Chroma, etc.)
- Strong understanding of hallucination mitigation, guardrails, and safety frameworks.
Core Engineering & Platform Skills
- Expert-level Python engineering (production-grade systems).
- Strong experience with cloud-native AI solutions on Azure, AWS, or GCP.
- API design, microservices, and event-driven architectures.
Mandatory Prior Background
-
Data Engineering or Data Science experience is non-negotiable, including:
-
Data pipelines / ETL / ELT / orchestration
-
ML or NLP model lifecycle
-
Analytics platforms or data product engineering
Good-to-Have / Preferred
- Fine-tuning and adaptation strategies (LoRA / PEFT / prompt tuning).
- Experience with MLOps / LLMOps platforms and observability stacks.
- Experience delivering GenAI solutions in regulated industries (Insurance, Healthcare, BFS).
- Exposure to enterprise AI governance frameworks.
About Us
EXL (NASDAQ: EXLS) is a leading data analytics and digital operations and solutions company. We partner with clients using a data and AI-led approach to reinvent business models, drive better business outcomes and unlock growth with speed. EXL harnesses the power of data, analytics, AI, and deep industry knowledge to transform operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media and retail, among others. EXL was founded in 1999 with the core values of innovation, collaboration, excellence, integrity and respect. We are headquartered in New York and have more than 54,000 employees spanning six continents. For more information, visit www.exlservice.com.
EXL never requires or asks for fees/payments or credit card or bank details during any phase of the recruitment or hiring process and has not authorized any agencies or partners to collect any fee or payment from prospective candidates. EXL will only extend a job offer after a candidate has gone through a formal interview process with members of EXL’s Human Resources team, as well as our hiring managers.
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