Tech Lead / Lead Architect – RAG & Agentic AI Urgent Need

Tech Mirrors; Columbus (OH); Hybrid; USD 180,000 - 230,000; Full time

Job summary

Tech Mirrors in Columbus, OH is seeking a Tech Lead / Lead Architect for a long-term project focused on Agentic AI and RAG-based solutions. The role will partner with customers and internal teams to design scalable, secure AI systems and oversee delivery across multiple stakeholders.

Must-have capabilities include hands-on experience with RAG pipelines, embeddings, vector databases, LLM orchestration, and strong AWS expertise (Lambda, API Gateway, Bedrock, S3, OpenSearch, IAM, VPC, Secrets

Qualifications

• Experience in building RAG pipelines and vector databases.
• Hands-on AWS architecture including Lambda, API Gateway, and Bedrock.
• Ability to design end-to-end AI architectures and deliver PoCs to customers.
• Strong understanding of AI guardrails, data privacy, and cloud security.
• Proven leadership, mentorship, and ability to deliver under tight timelines.
• Excellent customer communication to explain architectures, trade-offs and risks.
• Experience with model evaluation, observability, performance tuning, and cost optimization.
• Experience with API design, microservices, and event-driven AI systems.

Responsibilities

• Lead architecture and delivery of Agentic AI and RAG-based solutions.
• Mentor teams and own delivery with high visibility.
• Collaborate with customers and internal teams to ensure scalable, secure AI systems.

Skills

RAG pipelines

Embeddings

Vector databases

LLM orchestration

Prompt engineering

AWS

Lambda

API Gateway

Bedrock

S3

OpenSearch

IAM

VPC

Secrets Manager

AI guardrails

Data privacy

Cloud security

Leadership

Mentoring

Delivery ownership

Customer communication

Architecture design

PoC to production

Tools

LangGraph

CrewAI

AutoGen

Semantic Kernel

Job description

Job Title:Tech Lead / Lead Architect – RAG & Agentic AI

Location: Columbus, OH/ Wilmington, DE – 3 days onsite role

Long Term Project

Strong financial and banking domain experience must need.

Role Summary: Lead architecture, design, and delivery of Agentic AI and RAG-based solutions, partnering with customers and internal teams to build scalable, secure, and high-impact AI systems.

Must-Have

• Strong experience in RAG pipelines, embeddings, vector DBs, LLM orchestration, and prompting techniques.
• Hands-on expertise in AWS (Lambda, API Gateway, Bedrock, S3, OpenSearch, IAM, VPC, Secrets Manager).
• Ability to design end-to-end AI architecture and build PoCs before committing solutions to customers.
• Deep understanding of AI guardrails (toxicity, hallucination control), data privacy, and cloud security patterns.
• Proven ability to lead from the front, mentor teams, and own delivery under tight timelines and high visibility.
• Strong customer communication skills – ability to explain architecture, trade-offs, and risks clearly.
• Experience handling model evaluation, observability, performance tuning, and cost optimization in production AI systems.
• Expertise in API design, microservices integration, and event-driven architectures for AI systems.

Good-to-Have

• Experience with Agentic AI frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, etc.).
• Exposure to marketing domain use cases (campaign optimization, personalization, analytics, insights).
• Familiarity with multi-agent orchestration, tool usage (MCP), and human-in-loop workflows.

Screening Checklist (Quick Evaluation for Interviews)

Use this to quickly filter candidates:

Technical Fit

• Can clearly explain a RAG architecture (data ingestion → embedding → retrieval → generation)
• Has built or deployed production AI/LLM solutions (not just POCs)
• Understands agent lifecycle, orchestration, and tool integrations
• Demonstrates AWS architecture + security (IAM roles, network isolation, secrets)
• Knows prompt engineering + evaluation + guardrails implementation

Architect & Leadership Fit

• Has led architecture/design discussions with customers
• Can drive PoC → production transition independently
• Shows ownership mindset (decision-making without dependency)
• Can mentor/coach developers and review designs/code

Communication & Behavioral Fit

• Explains complex AI topics in simple, structured way
• Asks insightful, strategic questions
• Demonstrates ability to handle ambiguity and pressure