Technical Solutions Architect, Evals & Fine-Tuning

Role Description

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 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
• Earned credibility with sophisticated ML buyers

What You’ll Own:

• Lead technical discovery with prospective and existing customers 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
• Multimodal eval (text, image, audio, video, long-context)
• Architect engagements that combine Innodata’s platforms 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.
• Stay current on the state of the art in evals and post-training.
• Represent Innodata externally at customer reviews, conferences, and in technical content.

Qualifications

• 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.
• Strong fluency in Python and the modern LLM toolchain.
• Excellent technical communication skills.
• A consultative mindset.
• Bachelor’s or advanced degree in computer science, machine learning, computational linguistics, or related field — or equivalent demonstrated experience.

Requirements

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

Company Description

Innodata is a global data engineering company focused on enabling the responsible advancement of artificial intelligence.