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AI/ML Engineer – Remote I Contract I Hourly Pay I Long-Term Opportunity - Job Opportunity at Hermetic AI

Remote, CA
Contract
Senior
Posted: August 8, 2025
Remote
USD $40-$70 per hour

Key Responsibilities

Architect and deploy enterprise-scale LLM-powered applications from scratch, including autonomous agents, tool integrations, and production pipelines that directly impact business operations
Design and scale sophisticated Retrieval-Augmented Generation (RAG) pipelines using advanced vector databases and dynamic memory systems to enable intelligent information retrieval
Build complex LangGraph agents that orchestrate multi-step workflows, API integrations, and user interactions to automate decision-making processes
Analyze large-scale customer data to drive product optimization, extract actionable business insights, and enhance agent performance metrics
Collaborate cross-functionally with product and engineering teams to align AI system behavior with strategic business objectives and user experience goals
Lead rapid prototyping initiatives that transform conceptual AI solutions into production-ready systems within accelerated development cycles
Continuously optimize model performance across multiple dimensions including latency, reliability, cost efficiency, and resource utilization

Requirements

Education

Master's Degree (required)

Experience

Deep experience writing production-grade Python code

Required Skills

Deep experience writing production-grade Python code Experience building with LLMs like OpenAI, Claude, Mistral, or open-source models Working knowledge of LangGraph, LangChain, LlamaIndex, or similar orchestration frameworks Strong understanding of RAG, tool use, function calling, and agent design Experience with vector databases (Weaviate, Qdrant, Pinecone, etc.) Systems-level thinking — able to architect, scale, and maintain complex services A track record of building and shipping — ideally outside of academic environments
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Sauge AI Market Intelligence

Industry Trends

The AI/ML engineering market is experiencing unprecedented demand for professionals who can bridge the gap between experimental AI models and production-ready systems. Organizations are moving beyond proof-of-concept phases and requiring engineers who can deploy scalable LLM applications that deliver measurable business value. Retrieval-Augmented Generation (RAG) architectures are becoming the dominant paradigm for enterprise AI applications, as companies seek to combine the power of large language models with their proprietary data sources while maintaining accuracy and reducing hallucinations. The shift toward AI-native companies is creating a new category of technology businesses that are built from the ground up around artificial intelligence capabilities, requiring engineers who understand both the technical and business implications of AI system design. Vector databases and semantic search technologies are experiencing explosive growth as the foundational infrastructure for modern AI applications, with market projections showing continued expansion as more organizations implement RAG systems.

Salary Evaluation

The $40-$70 per hour range for this contract position translates to approximately $83,200-$145,600 annually, which is competitive for senior AI/ML engineers in the current market. Given the specialized LLM focus and production requirements, this falls within market expectations for contract work, though top-tier candidates with proven track records in similar roles could potentially negotiate toward the higher end of this range.

Role Significance

Based on the collaborative requirements and startup context, this role likely involves working within a small, agile engineering team of 3-8 people, with direct interaction with product stakeholders and potential mentorship responsibilities for junior team members.
This is a senior-level individual contributor role with significant autonomy and technical ownership. The position requires architecting complex systems from scratch and making critical technical decisions that directly impact business outcomes, indicating a high level of trust and responsibility within the organization.

Key Projects

Development of multi-agent AI systems for lead engagement automation in hospitality industry Implementation of large-scale RAG pipelines for customer data processing and insights generation Creation of real-time decision automation systems that reduce human operational overhead Design of scalable LLM inference infrastructure to support multiple client deployments

Success Factors

Demonstrated ability to ship production AI systems that handle real-world complexity and scale, moving beyond academic or experimental implementations Strong systems thinking combined with deep understanding of LLM limitations and optimization techniques to build reliable, cost-effective solutions Rapid iteration capabilities that allow for quick prototyping and deployment cycles while maintaining code quality and system reliability Business acumen to align technical decisions with product goals and understand the commercial implications of AI system design choices Collaborative leadership skills to work effectively with cross-functional teams and translate technical concepts for non-technical stakeholders

Market Demand

Extremely high demand exists for AI/ML engineers with production LLM experience, as the market has matured beyond research phases into implementation and scaling. The specific combination of LangGraph, RAG systems, and production deployment experience makes this a highly sought-after skill set with limited supply of qualified candidates.

Important Skills

Critical Skills

Production Python development skills are absolutely essential as the primary development language for AI/ML systems, with emphasis on writing maintainable, scalable code that can handle enterprise workloads and complexity LLM integration experience is critical given the core focus on language model applications, requiring understanding of model capabilities, limitations, API usage, and optimization techniques for various use cases RAG system design expertise is fundamental to the role as most enterprise AI applications require combining language models with proprietary data sources, making this a core competency for success Vector database management skills are essential for implementing semantic search and retrieval systems that form the backbone of modern AI applications

Beneficial Skills

Open-source contributions demonstrate technical leadership and community engagement, which is valuable for staying current with rapidly evolving AI tools and frameworks Experience with model fine-tuning and optimization provides additional technical depth that can be valuable for custom model development and performance enhancement Startup experience offers valuable context for working in fast-paced environments with limited resources and changing requirements Previous exposure to enterprise AI deployments provides understanding of production challenges including scalability, security, and compliance requirements

Unique Aspects

Direct involvement in building systems that power real-world business operations for major hospitality groups, providing immediate feedback on AI system effectiveness
Opportunity to work at the cutting edge of LLM application development during a period of rapid technological advancement and market adoption
Access to real customer data and use cases that provide insights into practical AI implementation challenges and solutions
Potential for significant equity upside in a fast-growing AI company during a period of market expansion
Exposure to multiple industry verticals as the company expands its AI automation platform beyond hospitality

Career Growth

Career progression to senior leadership roles typically occurs within 2-3 years for high-performing individuals in fast-growing AI companies, with opportunities for rapid advancement based on the critical nature of AI expertise in current market conditions.

Potential Next Roles

Senior AI/ML Architect with responsibility for technical strategy across multiple product lines Head of AI Engineering leading technical teams and setting organizational AI standards Technical Co-founder or CTO role at AI-focused startups leveraging deep technical expertise Principal Engineer or Staff Engineer positions at major technology companies building AI infrastructure

Company Overview

Hermetic AI

Hermetic AI represents the emerging category of AI-native companies that are built specifically around artificial intelligence capabilities rather than retrofitting AI into existing business models. The company focuses on practical applications that demonstrably reduce human workload, positioning itself in the growing market for AI-powered business automation.

As a post-revenue company with backing from industry operators, Hermetic AI appears to be in a strong position within the competitive AI startup landscape. The company's focus on real-world applications rather than research positions it well for sustainable growth and market traction.
Operating as a remote-first organization allows Hermetic AI to access global talent while maintaining operational efficiency. This approach is particularly advantageous in the competitive AI talent market where the best engineers are distributed worldwide.
The company culture emphasizes rapid execution, technical excellence, and practical results over theoretical research. The emphasis on shipping real systems and working with demanding customers suggests a high-performance environment that rewards results and innovation.
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