Workana

Freelancer marketplace connecting clients with professionals

AI Application Engineer

Natural Language Processing & LLMsConfirmed live in the last 24 hours

Part-timeRemote in Argentina

About the Job

Requirements

Hands-on experience personally fine-tuning open-weight language models using LoRA/QLoRA and full supervised fine-tuning, explaining trade-offs, and providing at least one shipped example.
Experience with training infrastructure using PyTorch and Hugging Face tools, including Transformers, PEFT, TRL or Axolotl; multi-GPU training with DeepSpeed or FSDP; and training workloads on AWS or GCP.
Expertise building instruction or preference datasets from unstructured documents, including deduplication, filtering, train/evaluation splits, and contamination prevention.
Experience designing domain-specific evaluation harnesses beyond standard benchmarks, including human-calibrated judge setups and regression testing.
Practical experience with preference or feedback learning methods such as DPO or RLHF-style workflows and integrating expert feedback into model updates.
Solid software engineering fundamentals, including production workflows with Git and pull requests, debugging, testing, deployment, and maintainable code.
Experience with APIs, databases, and service integration for model inference.
Ability to work independently, learn quickly, and follow technical direction.
Good English communication skills and availability to overlap with U.S. working hours.

Responsibilities

Design, implement, and run LLM fine-tuning experiments using LoRA/QLoRA and full supervised fine-tuning on open-weight models such as Llama, Mistral, and Qwen, and ship trained models into product workflows.
Build and maintain training infrastructure using PyTorch and Hugging Face tools, including Transformers, PEFT, TRL or Axolotl, multi-GPU training orchestration with DeepSpeed or FSDP, and AWS or GCP.
Engineer datasets from real-world unstructured sources such as long PDFs and medical or legal records, including deduplication, filtering, contamination checks, and train/evaluation splits.
Create domain-specific evaluation harnesses using held-out test sets, LLM-as-judge with human calibration, regression tests across model versions, and automated monitoring for model drift.
Implement preference or feedback training workflows such as DPO or RLHF-style methods to learn from expert corrections and integrate feedback loops into model retraining pipelines.
Collaborate with backend and frontend engineers to integrate fine-tuned models into services, optimize inference latency and cost, and support production deployments.
Participate in pull request reviews, release processes, incident debugging, and continuous improvement of training and deployment tools.
Ensure secure, compliant handling of sensitive data during dataset preparation and model training.

Desired Qualifications

Experience with long-context strategies for very large documents, including retrieval-aware training, context extension, or retrieval-augmented generation for multi-thousand-page sources.
Model deployment and inference optimization experience, including quantization with GPTQ/AWQ, vLLM or TGI serving, batching and throughput tuning, and latency and cost optimization.
Familiarity with vector databases, advanced retrieval-augmented generation pipelines, MCPs, or n8n-style workflow automation tools.
Knowledge of Docker, continuous integration and continuous delivery, Kubernetes/EKS, or serverless infrastructure.
Prior experience in healthcare, legal technology, HIPAA-aware processes, or other regulated or data-sensitive environments.
A public portfolio, GitHub profile, or examples of shipped LLM/agentic applications and fine-tuning projects.

AI Application Engineer (Part-Time)

Workana · Argentina (Remote) · Full-time · Posted 2026-06-23

Workplace: remote

Department: MedXprts

Description

Our client is building an AI-powered platform for the legal and healthcare space, using LLMs, agentic workflows, and automation to create production-grade applications.

We are seeking an AI Engineer - LLM Fine-Tuning: a hands-on engineer who has personally trained and fine-tuned open-weight models, built training infrastructure, engineered datasets from messy documents, and established rigorous evaluation and preference/feedback training pipelines. This role works closely with engineering teams to ship models and integrated features into production.

Responsibilities

Design, implement, and run LLM fine-tuning experiments (LoRA/QLoRA and full SFT) on open-weight models (e.g., Llama, Mistral, Qwen) and ship trained models into product workflows.
Build and maintain training infrastructure using PyTorch and Hugging Face tooling (Transformers, PEFT, TRL/Axolotl), including multi-GPU training orchestration (DeepSpeed/FSDP) on AWS or GCP.
Engineer datasets from real-world unstructured sources (long PDFs, medical/legal records), performing deduplication, filtering, contamination checks, and train/eval splits.
Create evaluation harnesses tailored to domain needs: held-out test sets, LLM-as-judge with human calibration, regression tests across model versions, and automated monitoring for model drift.
Implement preference/feedback training workflows (DPO/RLHF-style or similar) to learn from expert corrections (doctor-in-the-loop), and integrate feedback loops into model retraining pipelines.
Collaborate with backend/frontend engineers to integrate fine-tuned models into services, optimize inference latency/cost, and support production deployments.
Participate in PR reviews, release processes, incident debugging, and continuous improvement of training and deployment tooling.
Ensure secure, compliant handling of sensitive data (HIPAA-awareness is highly preferred) during dataset preparation and model training.

Requirements

Hands-on experience fine-tuning LLMs: personally trained or fine-tuned open-weight models using LoRA/QLoRA and full SFT; able to explain trade-offs and provide at least one shipped example.
Training infrastructure experience: PyTorch + Hugging Face ecosystem (Transformers, PEFT, TRL/Axolotl), multi-GPU training knowledge (DeepSpeed or FSDP), and running training workloads on AWS or GCP.
Dataset engineering expertise: built instruction/preference datasets from messy, unstructured documents; practical knowledge of deduplication, filtering, train/eval splits, and contamination prevention.
Strong evaluation discipline: designed domain-specific evaluation harnesses beyond standard benchmarks, including human-calibrated judge setups and regression testing.
Practical experience with preference/feedback learning methods (DPO, RLHF-style workflows, or equivalent) and integrating expert feedback into model updates.
Solid software engineering fundamentals: production workflows (Git, PRs), debugging, testing, deployment experience, and maintainable code.
Experience with APIs, databases, and service integration for model inference.
Ability to work independently, learn quickly, and follow technical direction.
Good English communication skills and availability to overlap with U.S. working hours.

Nice to Have

Experience with long-context handling strategies for very large documents (retrieval-aware training, context extension, RAG for multi-thousand-page sources).
Model deployment and inference optimization: quantization (GPTQ/AWQ), vLLM/TGI serving, batching/throughput tuning, latency and cost optimization.
Familiarity with vector databases, advanced RAG pipelines, MCPs, or n8n-style workflow automation tools.
Knowledge of Docker, CI/CD, Kubernetes/EKS, or serverless infrastructure.
Prior experience in healthcare, legaltech, HIPAA-aware processes, or other regulated/data-sensitive environments.
Public portfolio, GitHub, or examples of shipped LLM/agentic applications and fine-tuning projects.

Benefits

Fully remote role.
Opportunity to work on real AI products in the legal and healthcare domain.
High ownership and autonomy.
Performance-based incentives and outcome-driven bonuses.
Potential to grow into a long-term, full-time role.

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About the Company

Workana

Freelancer marketplace connecting clients with professionals

Workana connects businesses with independent professionals in Latin America through a freelancer marketplace. Clients post projects and hire talent across fields like IT, design, marketing, and admin support, while freelancers create profiles, showcase skills, and bid on opportunities. The platform earns commissions on completed projects and uses an escrow system to securely hold client payments until the work is finished to the client's satisfaction. It differentiates itself by focusing on the Latin American market, using freelancer rankings and tiers, and growing through regional expansion and partnerships to simplify hiring remote talent with secure transactions.

Company size: 5,001-10,000
Company stage: Series C
Total funding: $15.8M
Headquarters: Buenos Aires, Argentina
Founded: 2012

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