{"id":5961,"url":"https://alion.io/job/workana-ai-application-engineer-part-time","title":"AI Application Engineer (Part-Time)","company":{"id":2402,"name":"Workana","domain":"workana.com","url":"https://alion.io/company/workana","size_band":null,"is_staffing_agency":true,"employer_type":"agency","is_intermediary":true,"listed_via":null,"ats_vendor":"Workable","truth_index":{"grade":"A","score":92,"open_postings":29,"ghost_share":0,"stale_share":0.31,"repost_share":0,"time_to_fill_p50_days":58,"computed_at":"2026-10-29T05:49:05Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"part_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":{"label":"US time zones","utc_offset_min":-8,"utc_offset_max":-5},"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["AR","BR","CO","MX","CR","PE"],"hiring_countries_total":6,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AWS","optional":false},{"name":"Axolotl","optional":false},{"name":"DeepSpeed","optional":false},{"name":"DPO","optional":false},{"name":"Fine-tuning","optional":false},{"name":"FSDP","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"HIPAA","optional":false},{"name":"Hugging Face","optional":false},{"name":"Llama","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LoRA","optional":false},{"name":"Mistral","optional":false},{"name":"PEFT","optional":false},{"name":"PyTorch","optional":false},{"name":"QLoRA","optional":false},{"name":"Qwen","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Transformers","optional":false},{"name":"TRL","optional":false},{"name":"Amazon EKS","optional":true},{"name":"AWQ","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GitHub","optional":true},{"name":"GPTQ","optional":true},{"name":"Kubernetes","optional":true},{"name":"n8n","optional":true},{"name":"Quantization","optional":true},{"name":"RAG","optional":true},{"name":"TGI","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-06-23T00:00:00Z","employer_posted_date":"2026-06-23","last_verified_at":"2026-10-29T08:48:44Z","board_verified":true,"closed_at":null,"days_open":128,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":128},"description":"Client: medxprts.ai\nLocation: Remote\nType: Part-time, with potential to transition to Full-time\nSchedule: U.S. timezone overlap required\nDescription\nMedxprts.ai is building an AI-powered platform for the legal and healthcare space, using LLMs, agentic workflows, and automation to create production-grade applications.\nWe 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.\nResponsibilities\nDesign, 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.\nBuild 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.\nEngineer datasets from real-world unstructured sources (long PDFs, medical/legal records), performing deduplication, filtering, contamination checks, and train/eval splits.\nCreate 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.\nImplement 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.\nCollaborate with backend/frontend engineers to integrate fine-tuned models into services, optimize inference latency/cost, and support production deployments.\nParticipate in PR reviews, release processes, incident debugging, and continuous improvement of training and deployment tooling.\nEnsure secure, compliant handling of sensitive data (HIPAA-awareness is highly preferred) during dataset preparation and model training.\nRequirements\nHands-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.\nTraining 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.\nDataset engineering expertise: built instruction/preference datasets from messy, unstructured documents; practical knowledge of deduplication, filtering, train/eval splits, and contamination prevention.\nStrong evaluation discipline: designed domain-specific evaluation harnesses beyond standard benchmarks, including human-calibrated judge setups and regression testing.\nPractical experience with preference/feedback learning methods (DPO, RLHF-style workflows, or equivalent) and integrating expert feedback into model updates.\nSolid software engineering fundamentals: production workflows (Git, PRs), debugging, testing, deployment experience, and maintainable code.\nExperience with APIs, databases, and service integration for model inference.\nAbility to work independently, learn quickly, and follow technical direction.\nGood English communication skills and availability to overlap with U.S. working hours.\nNice to Have\nExperience with long-context handling strategies for very large documents (retrieval-aware training, context extension, RAG for multi-thousand-page sources).\nModel deployment and inference optimization: quantization (GPTQ/AWQ), vLLM/TGI serving, batching/throughput tuning, latency and cost optimization.\nFamiliarity with vector databases, advanced RAG pipelines, MCPs, or n8n-style workflow automation tools.\nKnowledge of Docker, CI/CD, Kubernetes/EKS, or serverless infrastructure.\nPrior experience in healthcare, legaltech, HIPAA-aware processes, or other regulated/data-sensitive environments.\nPublic portfolio, GitHub, or examples of shipped LLM/agentic applications and fine-tuning projects.\nBenefits\nFully remote role.\nOpportunity to work on real AI products in the legal and healthcare domain.\nHigh ownership and autonomy.\nPerformance-based incentives and outcome-driven bonuses.\nPotential to grow into a long-term, full-time 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