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AI/ML Engineer / Defence / $500k+ / NYC

Open Talent

New York · On-site Full-time Senior $350k – $500k/yr 3w ago

About the role

Senior AI/ML Engineer - (LLMs / RAG / Agentic Systems) - Aerospace - NYC - $500k+ Equity

We’re hiring for a world class team at one of the fastest growing & most innovative next‑gen aerospace AI companies in North America. They're building LLMs, RAG & high‑performance AI systems to power mission‑critical operations in defence & space. Only the top 0.1% of America's AI Engineers can do this. If you’ve built real mission critical LLM systems that can’t fail – this could be your next move.

The Role

  • You’ll own & scale a RAG‑driven AI system powering critical aerospace operations.
  • Expect to tackle retrieval quality, latency bottlenecks, & eval‑driven fine‑tuning head‑on for the most innovative & exciting real‑time systems in Aerospace today.
  • Full production ownership – from architecture to metrics.

You Should Have Experience With

  • Deploying LLM‑based systems at scale (Llama, Mistral, GPT‑4, Claude)
  • Retrieval optimisation – hybrid search (BM25 + vector), reranking, caching
  • Evaluation – recall@k, precision@k, groundedness, hallucination metrics
  • Fine‑tuning (LoRA/QLoRA), RAG architecture, LangChain / LlamaIndex
  • Tooling: Hugging Face, Weaviate/Pinecone, FastAPI, PyTorch, Redis, Postgres
  • Monitoring inference, eval automation, & continuous improvement loops

Bonus

  • IoT or Hardware integration or real‑time systems experience

Ideal Background

  • CS or Applied ML degree from a top‑tier university
  • Proven 0‑1 experience in a fast‑moving AI startup, or Defence related company
  • You’ve shipped reliable GenAI systems under pressure
  • Degree in Computer Science from a top‑tier university
  • Prior experience in high‑performance teams (0‑1, startups)
  • You’ve shipped real systems – not demos

You’ll Be Expected To

  • Own the LLM pipeline end‑to‑end – retrieval → evaluation → fine‑tuning
  • Improve accuracy, grounding, and eval metrics continuously
  • Make architecture decisions for scale, reliability & speed
  • Collaborate directly with founders & domain experts

Stack

  • Python / PyTorch / LangChain / Transformers / FastAPI / Hugging Face / Weaviate / Redis / Postgres / Docker / Kubernetes

Offer

  • Up to $500,000+ base salary + equity
  • Hybrid in NYC
  • Tight‑knit, high‑output team
  • High ownership, real impact, flat structure

Interview Process

  • Technical Deep Dive
  • Technical Assessment or Live Pairing
  • Panel focused on mindset & team fit

Equal Opportunity

We’re hiring for an Equal Opportunity Employer.

TL;DR – Senior AI Engineer (NYC, On‑site)

  • Build & scale production‑grade GenAI systems from 0 to 1
  • Own and improve RAG pipelines – accuracy and latency matter
  • Deep experience with LLMs, LangChain, Hugging Face & real deployments
  • Must have shipped working GenAI products, not just prototypes
  • Ideal: startup DNA, top‑tier CS degree, fast execution mindset
  • $350K+ base + equity – on‑site at a VC‑backed aerospace startup in NYC

Application

All our new jobs are posted here first:

  • linkedin.com/in/sufyanbashir/

Requirements

  • Deploying LLM-based systems at scale (Llama, Mistral, GPT-4, Claude)
  • Retrieval optimisation
  • hybrid search (BM25 + vector), reranking, caching
  • Evaluation
  • recall@k, precision@k, groundedness, hallucination metrics
  • Fine-tuning (LoRA/QLoRA), RAG architecture, LangChain / LlamaIndex
  • Monitoring inference, eval automation, & continuous improvement loops
  • Proven 0-1 experience in a fast-moving AI startup, or Defence related company
  • You’ve shipped reliable GenAI systems under pressure
  • Prior experience in high-performance teams (0-1, startups)
  • You’ve shipped real systems - not demos
  • Build & scale production-grade GenAI systems from 0 to 1
  • Own and improve RAG pipelines
  • Deep experience with LLMs, LangChain, Hugging Face & real deployments
  • Must have shipped working GenAI products, not just prototypes

Responsibilities

  • Own the LLM pipeline end-to-end – retrieval → evaluation → fine-tuning
  • Improve accuracy, grounding, and eval metrics continuously
  • Make architecture decisions for scale, reliability & speed
  • Collaborate directly with founders & domain experts

Skills

AWS LambdaBM25ClaudeDockerFastAPIGPT-4Hugging FaceKubernetesLangChainLlamaLlamaIndexLoRAMistralPineconePostgresPyTorchQLoRARAGRedisTransformersVectorWeaviate

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