Technical Solutions Architect, Evals & Fine-Tuning

InnoData

Technical Solutions Architect at Innodata responsible for leading AI/ML customer engagements and designing post-training solutions. Collaborating with research scientists and engineers to ensure solution alignment with client objectives.

Posted 7/27/2026full-timeRemote • 🇺🇸 United StatesSeniorLead💰 $140,000 - $160,000 per yearWebsite

Tech Stack

Tools & technologies

PythonPyTorch

About the role

Key responsibilities & impact

Lead technical discovery with prospective and existing customers — foundation model labs, frontier AI teams, and large enterprises — to understand model objectives, gaps, and constraints.
Design end-to-end solutions across the post-training stack: SFT data curation, preference data collection for RLHF/DPO, golden datasets, custom benchmarks, LLM-as-judge pipelines, human-in-the-loop evaluation, red teaming, and multimodal eval (text, image, audio, video, long-context).
Architect engagements that combine Innodata’s platforms (GenAI Test & Evaluation Platform, Annotation Platform, GenAI Workbench) with our global SME workforce across 85+ languages and domains.
Author technical proposals, SOWs, solution diagrams, and pricing models in partnership with sales, delivery, and finance.
Run technical workshops, POCs, and pilot designs that de-risk larger programs and prove value quickly.
Serve as the ongoing technical advisor during delivery, partnering with applied research scientists, AI/ML research engineers, language data scientists, and program managers to keep solutions aligned with the original intent.
Feed customer signal back into Innodata’s R&D and product roadmap — what benchmarks customers actually want, where eval methodology is breaking, what new fine-tuning paradigms are gaining traction.
Stay current on the state of the art in evals (e.g., dynamic and agentic benchmarks, capability vs. safety evals, long-context and tool-use evaluation) and post-training (SFT, RLHF, DPO, RLAIF, rejection sampling, distillation).
Represent Innodata externally — at customer reviews, conferences, and in technical content.

Requirements

What you’ll need

7+ years of experience in applied ML, ML engineering, ML research, or technical solutions roles, with at least 2+ years focused specifically on LLM evaluation and/or post-training.
Hands-on experience fine-tuning LLMs (SFT at minimum; preference optimization methods like RLHF, DPO, or KTO strongly preferred) and designing the data pipelines that feed them.
Deep familiarity with LLM evaluation methodology: public benchmarks and their limitations, custom benchmark construction, LLM-as-judge design and its failure modes, inter-annotator agreement, and human eval workflow design.
Strong fluency in Python and the modern LLM toolchain (Hugging Face, PyTorch, vLLM, evaluation frameworks such as lm-evaluation-harness, lighteval, or equivalents).
Excellent technical communication. You can hold your own in a room with research scientists at a frontier lab and, an hour later, brief a non-technical executive on the same engagement.
A consultative mindset: you ask sharp questions, you push back when a customer’s stated request won’t actually solve their problem, and you are comfortable owning a recommendation.
Bachelor’s or advanced degree in computer science, machine learning, computational linguistics, or related field — or equivalent demonstrated experience.

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account InnoData Website LinkedIn All Job Openings 2 - 10 employees Founded 2019 🤝 B2B 💼 Consulting 🌍 Social Impact B2B
Consulting
Social Impact InnoData is INNOvation DATA SCS, an Italian social cooperative based in Foggia that identifies itself as a provider of technological solutions. The company website (currently under maintenance) highlights "Soluzioni tecnologiche" (technological solutions) and emphasizes social impact ("Impatto sociale"). Contact details listed include Via Francesco Crispi 65, 71121 Foggia, Italy. Based on the available information, InnoData appears to operate at the intersection of technology and social impact, likely offering tech-focused services to other organizations. Technical Solutions Architect, Evals & Fine-Tuning Job not on LinkedIn 🔥 2 minutes ago 🇺🇸 United States – Remote 💵 $140k - $160k / year ⏰ Full Time 🟠 Senior 🔴 Lead 💻 Solutions Engineer Python PyTorch Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Lead technical discovery with prospective and existing customers — foundation model labs, frontier AI teams, and large enterprises — to understand model objectives, gaps, and constraints.
Design end-to-end solutions across the post-training stack: SFT data curation, preference data collection for RLHF/DPO, golden datasets, custom benchmarks, LLM-as-judge pipelines, human-in-the-loop evaluation, red teaming, and multimodal eval (text, image, audio, video, long-context).
Architect engagements that combine Innodata’s platforms (GenAI Test & Evaluation Platform, Annotation Platform, GenAI Workbench) with our global SME workforce across 85+ languages and domains.
Author technical proposals, SOWs, solution diagrams, and pricing models in partnership with sales, delivery, and finance.
Run technical workshops, POCs, and pilot designs that de-risk larger programs and prove value quickly.
Serve as the ongoing technical advisor during delivery, partnering with applied research scientists, AI/ML research engineers, language data scientists, and program managers to keep solutions aligned with the original intent.
Feed customer signal back into Innodata’s R&D and product roadmap — what benchmarks customers actually want, where eval methodology is breaking, what new fine-tuning paradigms are gaining traction.
Stay current on the state of the art in evals (e.g., dynamic and agentic benchmarks, capability vs. safety evals, long-context and tool-use evaluation) and post-training (SFT, RLHF, DPO, RLAIF, rejection sampling, distillation).
Represent Innodata externally — at customer reviews, conferences, and in technical content. 🎯 Requirements
7+ years of experience in applied ML, ML engineering, ML research, or technical solutions roles, with at least 2+ years focused specifically on LLM evaluation and/or post-training.
Hands-on experience fine-tuning LLMs (SFT at minimum; preference optimization methods like RLHF, DPO, or KTO strongly preferred) and designing the data pipelines that feed them.
Deep familiarity with LLM evaluation methodology: public benchmarks and their limitations, custom benchmark construction, LLM-as-judge design and its failure modes, inter-annotator agreement, and human eval workflow design.
Strong fluency in Python and the modern LLM toolchain (Hugging Face, PyTorch, vLLM, evaluation frameworks such as lm-evaluation-harness, lighteval, or equivalents).
Excellent technical communication. You can hold your own in a room with research scientists at a frontier lab and, an hour later, brief a non-technical executive on the same engagement.
A consultative mindset: you ask sharp questions, you push back when a customer’s stated request won’t actually solve their problem, and you are comfortable owning a recommendation.
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