Machine Learning Engineer

TekVizor; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Qualifications

• 3+ years building and deploying Agentic, ML / LLM-powered systems in production environments.
• Degree in Computer Science or similar.
• Build APIs, microservices, and distributed workflows.
• Work across cloud environments (AWS, Azure, GCP).
• Ability to integrate data pipelines, messaging systems, and storage layers.
• You are as comfortable with backend architecture as with LLM integration.

Responsibilities

• Own the architecture, design, and implementation of full AI systems, including: Multi-Agent, distributed system architecture, workflow, agent orchestration, tool use, code execution, retrieval, KV cache management, routing, batching, multi-step planning loops.
• Proven experience designing and deploying complex agentic systems using LLMs.
• Hands-on design with multi-agent orchestration, routing, and tool orchestration.
• Your work goes far beyond prompts or app-level features. You will architect platforms and sustainable AI infrastructure.
• You will build real, production agents—not prototypes relying solely on framework abstractions.
• Design knowledge graph analytics enabling agents to perform contextual reasoning.
• End-to-End AI System Design.

Skills

Agentic ML/LLM systems

API development

Distributed architectures

Cloud environments (AWS, Azure, GCP)

Data pipelines & messaging

LLM integration

Backend architecture

Education

Degree in Computer Science or similar

Job description

New York, NYC (4 days in the NYC office)

About the Role

Hiring a Senior AI/ML Engineer for an early-stage, high-growth AI FinTech company. We're building an enterprise-grade platform and we need highly curious, hard-working self-starters.

This role is for senior-level builders who design, architect, and ship production-grade AI systems.

You will own the design and delivery of complex, multi-service AI architectures involving agent orchestration, secure workflows and real-world operational constraints.

What You Will Do

• Own the architecture, design, and implementation of full AI systems, including: Multi-Agent, distributed system architecture, workflow, agent orchestration, tool use, code execution, retrieval, KV cache management, routing, batching, multi-step planning loops.
• Proven experience designing and deploying complex agentic systems using LLMs.
• Hands-on design with multi-agent orchestration, routing, and tool orchestration.
• Your work goes far beyond prompts or app-level features. You will architect platforms and sustainable AI infrastructure.
• You will build real, production agents—not prototypes relying solely on framework abstractions.
• Design knowledge graph analytics enabling agents to perform contextual reasoning.
• End-to-End AI System Design.

Desired Qualifications

• 3+ years building and deploying Agentic, ML / LLM-powered systems in production environments.
• Degree in Computer Science or similar.
• Build APIs, microservices, and distributed workflows.
• Work across cloud environments (AWS, Azure, GCP)
• Ability to integrate data pipelines, messaging systems, and storage layers.
• You are as comfortable with backend architecture as with LLM integration.