AI, LLM and Machine Learning Engineering Search

Since 2023 nearly every engineering organization has posted an AI role, and most of them have written the same job description: a research pedigree, a list of frameworks, and a vague mandate to "leverage LLMs." The result is a market full of searches that stall for months, because the requirement on paper does not describe the work the company actually needs done. TradeTech Search Group starts by separating those two things.

Research talent and production talent are different hires

A machine learning researcher optimizes a metric on a benchmark. An applied ML engineer takes a model that is good enough and makes it survive real traffic, real latency budgets, real data drift, and real on-call rotations. Both are valuable; almost no company needs the first one when what is failing is the second. In practice, most of the mandates we run are production-engineering mandates. When a client insists on a research profile for a production problem, we say so during intake rather than spending three months proving it.

The same discipline applies to LLM hiring. Retrieval-augmented generation, evaluation harnesses, prompt and context engineering, guardrails, tool-calling agents, and inference cost control are engineering problems with an established body of practice. We screen for whether a candidate has shipped and maintained those systems under load, not whether they can describe the transformer architecture.

Roles in this practice

• Applied ML / Machine Learning Engineer: feature pipelines, training infrastructure, model serving, monitoring and retraining loops.
• LLM / Applied AI Engineer: RAG architectures, evaluation and regression testing, agent orchestration, fine-tuning and inference optimization.
• MLOps and Platform Engineering: the reproducibility, CI/CD, observability, and GPU-cost discipline that keeps model teams shipping.
• Computer Vision and Perception: the overlap with our robotics practice: detection, segmentation, sensor fusion, and edge deployment.
• Data Engineering: the pipelines and contracts that determine whether any model above has usable inputs.
• Head of AI / Director of Machine Learning: leaders who can set a build-versus-buy strategy, defend a model roadmap to a board, and hire.

Where the Northeast market actually is

The regional AI market splits into three buyers with very different pitches. Well capitalized platform companies compete on compensation and compute access. Enterprises (insurance, finance, healthcare, and industrial groups across Connecticut, New York and Massachusetts) compete on scope, offering an engineer the chance to own an AI function from scratch. Early-stage companies compete on equity and autonomy. Losing a candidate is usually a positioning failure, not a money failure, and part of our intake is helping you articulate which of those three stories you are credibly telling.

We also supply compensation reality. Applied ML and LLM engineering bands in this region moved sharply upward and have since stabilized unevenly by vertical. If your target range is below market for the profile you described, you will hear it from us in week one, with comparable data attached.

Engagement models

Leadership and first-hire mandates, where a wrong hire sets a roadmap back a year, are best run as a retained search with an exclusive, partner-led process. Individual contributor ML and data engineering roles often run efficiently as contingency search. Our four-step process is identical either way.