What are the responsibilities and job description for the AI Tech Lead / AI Architect (Production & Deployment) position at Straive?
Role : AI Tech Lead / AI Architect (Full Time/FTE)
Location :Newark, NJ
Experience : 8 to 14 years
Work Model
Hybrid (3 days in a week from office)
Primary Skill
Generative AI delivery pods, Azure OpenAI / AI Foundry,Production experience with Azur AI, deployed and AI Agent in production , LLM application patterns ,Python (FastAPI, AsyncIO), Docker
Secondary Skill
open source models
About The Role
We are seeking a hands-on
AI Tech Lead
to lead one or more Generative AI delivery pods for a financial-services client. The role sits between our Senior AI Engineers and the Engagement Manager.
You own the technical direction and day-to-day delivery of your pod: you design the architecture, review the work and unblock it, and you are the technical voice with client architects, platform and security teams. You turn business asks into designs, plans and estimates, and you are accountable for what goes to production.
Key Responsibilities
Core Technical Competencies
Generative AI & LLMs
Hands-on depth in LLM application patterns (RAG, hybrid search and re-ranking, agents and tools/MCP, structured outputs) and evaluation/observability tooling (RAGAS, LangSmith, Langfuse or similar).
Azure AI Platform
Production experience with Azure OpenAI / AI Foundry, AI Search, Cosmos DB, Functions / App Service, Key Vault, private networking and Entra ID / managed identity. AWS experience is acceptable with a clear path to Azure.
Responsible AI & Security
Content filters and guardrails, prompt-injection and data-leakage risks, PII handling, access control and audit logging in a regulated (financial services) environment.
Backend & DevOps
Strong Python (FastAPI, AsyncIO), Docker, Terraform, CI/CD and environment promotion (dev / UAT / prod) with rollback.
Communication
Clear written and spoken communication with technical and non-technical stakeholders.
Technical Leadership & Architecture
Solution Architecture
Own the architecture for GenAI applications (RAG, agentic workflows, document intelligence, evaluation and guardrails) on Azure: Azure OpenAI / AI Foundry, AI Search, Cosmos DB, Functions / App Service, APIM or an AI gateway.
Engineering Standards
Set and enforce branching and PR reviews, testing, IaC (Terraform), CI/CD (GitHub Actions), observability, security and cost practices.
Trade-off Decisions
Make and document the calls on model choice and routing, retrieval design, latency and cost, and build versus buy, so the team and the client can follow them.
Evaluation & Guardrails
Build evaluation in from the start: golden datasets, regression gates on every prompt or code change, LLM-as-judge where it fits, content filters and human review for high-risk outputs.
Production Readiness
Run release readiness: UAT support, release notes, rollback plans and production hand-off.
People, Delivery & Client Leadership
Delivery Ownership
Estimate, plan and track delivery; surface risks and dependencies early (environments, access, client approvals) and drive them to closure.
Client Technical Lead
Act as the senior technical point of contact with client architects, platform, security and governance teams; lead design walkthroughs, demos and technical decisions.
Scoping & Communication
Translate vague business asks into scoped solutions and phased plans, push back with options when a request is not feasible, and work with the Engagement Manager on scope, staffing and status.
Required Experience And Qualifications
Overall Professional Experience:
8-12 years in software or data/ML engineering, with progressive technical leadership.
AI/ML & GenAI Experience
3 years building and shipping Generative AI applications to production.
Team Leadership
At least 2 years leading engineers: allocation, reviews, mentoring and delivery accountability.
Preferred Experience & Education
Financial services or asset-management domain experience.
AI gateway / API management (APIM, Kong), model routing and cost control; document intelligence and OCR (Azure Document Intelligence / Content Understanding).
Working knowledge of React for chat and operations UIs.
Location :Newark, NJ
Experience : 8 to 14 years
Work Model
Hybrid (3 days in a week from office)
Primary Skill
Generative AI delivery pods, Azure OpenAI / AI Foundry,Production experience with Azur AI, deployed and AI Agent in production , LLM application patterns ,Python (FastAPI, AsyncIO), Docker
Secondary Skill
open source models
About The Role
We are seeking a hands-on
AI Tech Lead
to lead one or more Generative AI delivery pods for a financial-services client. The role sits between our Senior AI Engineers and the Engagement Manager.
You own the technical direction and day-to-day delivery of your pod: you design the architecture, review the work and unblock it, and you are the technical voice with client architects, platform and security teams. You turn business asks into designs, plans and estimates, and you are accountable for what goes to production.
Key Responsibilities
Core Technical Competencies
Generative AI & LLMs
Hands-on depth in LLM application patterns (RAG, hybrid search and re-ranking, agents and tools/MCP, structured outputs) and evaluation/observability tooling (RAGAS, LangSmith, Langfuse or similar).
Azure AI Platform
Production experience with Azure OpenAI / AI Foundry, AI Search, Cosmos DB, Functions / App Service, Key Vault, private networking and Entra ID / managed identity. AWS experience is acceptable with a clear path to Azure.
Responsible AI & Security
Content filters and guardrails, prompt-injection and data-leakage risks, PII handling, access control and audit logging in a regulated (financial services) environment.
Backend & DevOps
Strong Python (FastAPI, AsyncIO), Docker, Terraform, CI/CD and environment promotion (dev / UAT / prod) with rollback.
Communication
Clear written and spoken communication with technical and non-technical stakeholders.
Technical Leadership & Architecture
Solution Architecture
Own the architecture for GenAI applications (RAG, agentic workflows, document intelligence, evaluation and guardrails) on Azure: Azure OpenAI / AI Foundry, AI Search, Cosmos DB, Functions / App Service, APIM or an AI gateway.
Engineering Standards
Set and enforce branching and PR reviews, testing, IaC (Terraform), CI/CD (GitHub Actions), observability, security and cost practices.
Trade-off Decisions
Make and document the calls on model choice and routing, retrieval design, latency and cost, and build versus buy, so the team and the client can follow them.
Evaluation & Guardrails
Build evaluation in from the start: golden datasets, regression gates on every prompt or code change, LLM-as-judge where it fits, content filters and human review for high-risk outputs.
Production Readiness
Run release readiness: UAT support, release notes, rollback plans and production hand-off.
People, Delivery & Client Leadership
Delivery Ownership
Estimate, plan and track delivery; surface risks and dependencies early (environments, access, client approvals) and drive them to closure.
Client Technical Lead
Act as the senior technical point of contact with client architects, platform, security and governance teams; lead design walkthroughs, demos and technical decisions.
Scoping & Communication
Translate vague business asks into scoped solutions and phased plans, push back with options when a request is not feasible, and work with the Engagement Manager on scope, staffing and status.
Required Experience And Qualifications
Overall Professional Experience:
8-12 years in software or data/ML engineering, with progressive technical leadership.
AI/ML & GenAI Experience
3 years building and shipping Generative AI applications to production.
Team Leadership
At least 2 years leading engineers: allocation, reviews, mentoring and delivery accountability.
Preferred Experience & Education
Financial services or asset-management domain experience.
AI gateway / API management (APIM, Kong), model routing and cost control; document intelligence and OCR (Azure Document Intelligence / Content Understanding).
Working knowledge of React for chat and operations UIs.