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Particle41

AI Engineer

AI Summary
Particle41 is seeking an AI Engineer to build, deploy, and operate production-grade machine learning, deep learning, and generative AI solutions. The role spans the full ML lifecycle, including data preparation, training, evaluation, API and microservice integration, monitoring, and iterative improvement. Key work includes RAG systems, agentic workflows, MCP servers, conversational agents, vector databases, and LLM fine-tuning. The engineer will partner with data and software teams as well as client stakeholders to translate business problems into scalable AI products. This is a fast-paced, client-facing role requiring strong Python, MLOps, and production engineering capabilities.
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Remote from
USA
Salary
Undisclosed
Department
Data Science & Analytics
Employment
Full Time
Experience
Senior
Published
Apply before
11 Dec 2026
Listing views
28
Application actions
2
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Opportunity details

About this role.

AI Engineer

Role Overview:

As an AI Engineer at Particle41 you will design, develop and deploy machine-learning and deep-learning models/solutions (including generative-AI) that address complex business problems. You will collaborate with data scientists, data engineers, software engineers and client stakeholders. You will also build scalable pipelines, ensure production readiness, monitor performance and iterate proactively.

In This Role, You Will:

  • Lead end-to-end development of AI/ML models: from data ingestion & preprocessing to model training, evaluation, deployment and monitoring.
  • Build generative-AI solutions (RAG, Agentic Workflows, MCP Servers, Conversation AI Agents) aligned with business goals.
  • Work closely with data engineering teams to build/maintain data pipelines, feature stores and orchestration frameworks.
  • Develop and integrate AI models into production systems (APIs, microservices, cloud deployments).
  • Optimize models/solutions for performance, scalability, robustness and cost-efficiency.
  • Monitor models/ solutions’ performance in production (drift, bias, fairness, reliability) and implement remediation.
  • Document model design, experiments, data provenance and solution rationale.
  • Stay abreast of latest AI/ML research, frameworks, tools (e.g. LangChain, LangGraph, MCP Clients/Servers, Agents SDKs, LiveKit etc.) and propose innovative ideas.
  • Work directly with clients to understand problem statements, translate business requirements into AI solutions, and clearly communicate results.

Skills and Experience We Value:

  • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
  • 3+ years of hands-on experience in AI/ML model development and deployment.
  • Strong programming skills in Python and experience with major ML/AI frameworks (TensorFlow, PyTorch, scikit-learn, LangChain, LangGraph).
  • Experience with vector databases (e.g., Pinecone, FAISS), RAG and Agentic workflows.
  • Experience building or fine-tuning Large Language Models (LLMs).
  • Experience deploying models into production: building REST APIs, microservices and monitoring.
  • Familiarity with Text-to-Speech and Speech-to-Text models/solutions like Deepgram, ElevenLabs, Cartesia etc.
  • Experience in computer vision, time-series modelling (ARIMA, Prophet) or multimodal AI.
  • Familiarity with MLOps tools and frameworks (e.g., MLflow, Kubeflow, SageMaker).
  • Strong understanding of algorithms, data structures, statistics, and machine-learning fundamentals (classification, regression, clustering, deep learning, sequence models).
  • Ability to work in a fast-paced, dynamic environment and adapt to changing priorities.
  • Publications, open-source contributions, or personal projects in the AI/ML domain

About Particle41

Our core values of Empowering, Leadership, Innovation, Teamwork, and Excellence drive everything we do to achieve the ultimate outcomes for our clients. Empowering Leadership for Innovation in Teamwork with Excellence ( ELITE )

E – Empowering: Enabling individuals to reach their full potential. L – Leadership: Taking initiative and guiding each other toward success. I – Innovation: Embracing creativity and new ideas to stay ahead. T – Teamwork: Collaborating with empathy to achieve common goals. E – Excellence: Striving for the highest quality in everything we do.

We seek team members who embody these values and are committed to contributing to our mission. Particle41 welcomes individuals from all backgrounds who are committed to our mission and values. We provide equal employment opportunities to all employees and applicants, ensuring that hiring and employment decisions are based on merit and qualifications without discrimination based on race, color, religion, caste, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, local, or international laws. This policy applies to all aspects of employment and hiring.

We appreciate your interest and encourage applicants from these regions to apply. If you need any assistance during the application or interview process, please feel free to reach out to us at careers@Particle41.com.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe position requires broad, production-level expertise across ML engineering, generative AI, cloud deployment, MLOps, and client consulting. Success depends on independently delivering reliable solutions while navigating evolving tools and business requirements.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$140,000
US market range (per year)$120k–$165k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary was disclosed. Estimated annual base-pay median is USD 140,000, with an estimated US market range of USD 120,000 to USD 165,000 for an AI Engineer with 3+ years of production ML and generative AI experience.

Core skills

Skills and capabilities most closely associated with this opportunity.

PythonMachine LearningDeep LearningGenerative AILarge Language ModelsRetrieval-Augmented GenerationLangChainLangGraphVector DatabasesMLOpsPyTorchTensorFlowREST APIsModel MonitoringMicroservices
Sample interview questions
How would you design a production RAG solution for a client knowledge base?

I would start by clarifying user tasks, quality targets, security constraints, and source-data ownership. I would build ingestion and chunking pipelines, generate embeddings, store them in a vector database, implement retrieval and reranking, and connect the context to an LLM with grounded prompting. I would then evaluate answer quality, citation accuracy, latency, cost, and hallucination rates before deploying monitoring and feedback loops.

What practices do you use to monitor ML models after deployment?

I track operational metrics such as latency, error rate, throughput, and cost alongside model metrics such as accuracy, relevance, drift, bias, and reliability. I establish baselines and alert thresholds, log inputs and outputs with appropriate privacy controls, and investigate degradation through reproducible evaluation datasets. When needed, I implement rollback, retraining, prompt updates, or data-pipeline remediation.

Describe how you would deploy an LLM-powered service as a scalable API.

I would package the service in containers, expose clear REST or asynchronous endpoints, and separate orchestration, retrieval, model-provider access, and business logic into maintainable components. I would use authentication, rate limits, structured logging, caching, retries, and observability to support reliability. Horizontal scaling, queue-based background processing, and cost controls would be selected based on expected traffic and latency requirements.

How do you decide whether to fine-tune an LLM or use prompting and RAG?

I first evaluate whether the gap is factual knowledge, task behavior, format consistency, or domain language. RAG is usually preferable for changing or proprietary knowledge because it can provide current, traceable context, while prompting can solve many behavior and workflow needs quickly. I consider fine-tuning when a stable task requires consistent behavior or specialized output patterns that cannot be achieved reliably through prompting and retrieval alone.

How do you translate an ambiguous client problem into an AI solution?

I begin with stakeholder interviews to define the user, decision process, current workflow, data availability, risks, and measurable business outcomes. I convert these findings into a scoped use case, success metrics, architecture options, and a phased proof of concept. I communicate assumptions and trade-offs clearly, validate results with users, and prioritize an implementation that is practical, measurable, and maintainable.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
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