Technical Solutions Architect, Evals & Fine-Tuning

Scope of the Role: 

Innodata partners with leading foundation model labs, hyperscalers, and enterprise AI teams to build the data, evaluation, and post-training systems that make modern LLMs trustworthy and production-ready.  

As a Technical Solutions Architect for Evals & Fine-Tuning, you are the technical face of Innodata to our most demanding customers. You sit at the intersection of client AI/ML teams, our research scientists and ML engineers, our subject-matter expert workforce, and our platform teams. You translate ambiguous customer goals — “improve factuality on long-context legal QA,” “build a safety eval suite for our next model release,” “design a DPO pipeline for our coding assistant” — into concrete, scoped, deliverable engagements. 

This is a senior individual-contributor role for someone who has done the work: built fine-tuning pipelines, designed eval harnesses, argued with stakeholders about benchmark validity, and earned credibility with sophisticated ML buyers. 

What You’ll Own:

• 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. 

You’ll Thrive in This Role If You Have:

• 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. 

The expected salary range for this position is $140,000 – $160,000 USD per year, based on experience, skills, and qualifications.