VP AI/ML Software Engineer – LLM Engineer / GenAI Engineer - Fulltime

Hiring: Senior AI Engineer – Agentic AI & LLM Systems (Backend + Architecture)

Locations: Jersey City, NJ | Dallas, TX | Menlo Park, CA | Seattle, WA (Onsite)

Full-Time

$150K – $200K Base + Bonus

We are seeking a hands-on Senior AI Engineer to design, build, and deploy production-grade agentic AI systems at scale. This role requires a strong combination of backend engineering, LLM system design, and architectural thinking.

This is an individual contributor role for engineers who can not only build systems but also clearly articulate high-level design, tradeoffs, and production considerations.

What You’ll Do:

• Design and implement end-to-end AI systems, including ingestion, retrieval, orchestration, and evaluation layers

• Build and deploy multi-agent AI systems (planning, reasoning, tool usage, coordination)

• Develop scalable backend services and APIs for AI-driven applications

• Architect RAG pipelines and hybrid retrieval systems for enterprise use cases

• Define and implement LLMOps practices, including evaluation frameworks, monitoring, and observability

• Optimize systems for latency, scalability, cost, and reliability

• Integrate AI systems with cloud infrastructure, distributed systems, and enterprise platforms

• Collaborate with cross-functional teams to translate business needs into scalable AI solutions

Required Qualifications:

• 8–15 years of experience in software engineering, AI/ML engineering, or platform engineering

• Strong programming skills in Python (required)

• Proven experience building production-grade distributed systems and APIs

• Hands-on experience with LLMs and GenAI systems in production environments

MUST-HAVE SKILLS:

• Experience designing and building multi-agent AI systems (LangGraph, CrewAI, AutoGen, MCP, or similar)

• Strong understanding of LLM system architecture, including orchestration, memory, retrieval, and tool usage

• Experience with RAG pipelines, vector databases, and hybrid retrieval strategies

• Experience implementing LLM evaluation frameworks (LLM-as-judge, hallucination detection, benchmarking)

• Strong knowledge of cloud platforms (AWS preferred) and distributed systems (Kubernetes, Docker)

• Experience with event-driven architectures (Kafka, streaming pipelines, async processing)

Preferred Qualifications:

• Experience building enterprise-scale AI platforms

• Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry)

• Exposure to financial services or regulated environments

• Experience explaining system design and tradeoffs to both technical and non-technical stakeholders

Not a Fit If:

• You are primarily a data scientist or research-focused ML engineer

• Limited experience with production system design and deployment

• Experience is limited to POCs, demos, or basic chatbot/RAG implementations

• Primarily focused on people management or high-level strategy without hands-on coding

This role is ideal for engineers who can design, build, and explain production AI systems end-to-end — combining deep technical execution with strong system-level thinking.