Agentic/AI lead/architect with Claude/code/LLM skills1
Hybrid Noida Full-time Senior AI Engineering
At a glance
Senior AI Engineering role at Fa Ewjt Saasfaprod1. Noida · full-time.
Pay not stated AI Engineering salary
RoleAI Engineering
SenioritySenior
LocationNoida
WorkplaceHybrid
EmploymentFull-time
PostedMay 27, 2026 · 4mo ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Set reference designs for single- and multi-agent systems, including tool use, memory, planning and execution
- Choose Claude and other LLMs, then guide their deployment while weighing cost and response time
- Build secure GenAI designs with privacy protections, guardrails and controlled access to tools
- Shape RAG pipelines from data preparation through search, testing and feedback, alongside Data Engineering teams
- Guide production practices, cloud deployments and engineering standards while advising clients and mentoring technical teams
What you bring
- At least twelve years overall, with hands-on Generative AI or LLM work and a prior Data Engineering or Data Science background
- Strong Python skills for production systems, plus experience building cloud AI solutions on Azure, AWS or GCP
- You have designed agent-based systems and RAG solutions using vector databases
- You bring experience with prompt and agent workflows, tool calling, LLM evaluation and safety controls
Who this fits
This senior technical leadership role suits someone who can set architecture direction while staying hands-on and mentoring experienced engineers. It is based in Noida, India, with a hybrid arrangement. Your background must include Data Engineering or Data Science.
From the employer
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.
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.
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.