AI/ML Engineer — NLP, LLM Systems & Applied Intelligence

Upcoming Role · Remote (USA or Europe)

The way information is discovered online is changing. AI-generated answers increasingly influence what gets surfaced, how brands are represented, and which sources are used to shape decisions across search and discovery environments. Ansvisor helps organizations understand that visibility and improve how they show up across this new layer of the web.

As we continue building our product and data capabilities, we expect applied AI and machine learning to play a growing role in how we analyze content, interpret visibility signals, structure unstructured data, and power customer-facing intelligence. We anticipate opening an AI/ML Engineer role to help design, build, and ship the systems behind that work.

This role will likely focus on production-grade NLP and LLM workflows that support tasks such as classification, extraction, ranking, clustering, summarization, and generation. We’ll be looking for someone who can move from raw data to deployed capability, build strong evaluation frameworks, and work closely with product and engineering to turn applied ML into useful product outcomes.

This is a strong fit for someone who enjoys shipping practical ML systems, working with large text datasets, and solving problems where model quality, product usefulness, and system reliability all matter.

About the Role

This role will sit close to the core of how Ansvisor processes information and turns it into structured intelligence. You’ll likely work on systems that help analyze content, interpret AI-generated outputs, organize large-scale language data, and support workflows that make the product more useful and scalable.

You may work across:

• NLP pipelines for classification, ranking, clustering, extraction, and summarization
• LLM workflows for analysis, synthesis, and generation
• Prompting and orchestration systems
• Evaluation frameworks for quality, consistency, and reliability
• Data pipelines and feature generation from large text datasets
• Experimentation around model quality, latency, and cost
• Collaboration with product, engineering, and data stakeholders

This is not a purely research-oriented role. We’ll be looking for someone who can build systems that operate reliably in production and create measurable value.

What We’ll Likely Look For

We’re interested in people who can combine ML depth with engineering practicality.

You may be a strong fit if you:

• Have experience shipping machine learning systems into production
• Are comfortable working with large text datasets and unstructured information
• Know how to design and improve NLP or LLM-based workflows for real product use cases
• Can build systems for classification, extraction, ranking, clustering, summarization, or generation
• Understand the importance of evaluation, observability, and failure handling in applied ML systems
• Are comfortable balancing model quality, latency, cost, and operational reliability
• Work well cross-functionally and can connect technical choices to product outcomes
• Bring strong ownership and are comfortable operating in ambiguous environments

We’ll value someone who is technically strong, product-aware, and capable of turning applied AI into dependable systems.

What You’d Be Expected to Do

While this role is not open yet, we expect responsibilities to include:

Applied NLP and LLM Systems

• Designing and deploying machine learning workflows for language-heavy product use cases
• Building systems for classification, clustering, extraction, ranking, summarization, and related tasks
• Developing LLM-based pipelines that help structure, interpret, and generate useful outputs from complex data
• Improving system quality through iteration on prompts, orchestration, model selection, and workflow design

Data-to-Deployment Ownership

• Working across the full path from raw data through processing, experimentation, and production deployment
• Turning large-scale text datasets into usable product signals, features, or automation layers
• Partnering with engineering and data teams to maintain robust pipelines and dependable delivery
• Supporting reliability, versioning, and maintainability across ML workflows

Evaluation and Quality Frameworks

• Building evaluation systems for output quality, accuracy, relevance, consistency, tone, and safety where relevant
• Creating practical approaches for offline testing, human review, and ongoing production monitoring
• Identifying failure patterns and improving workflows based on observed behavior
• Helping define what “good” looks like across different model-powered product capabilities

Product and Cross-Functional Collaboration

• Working closely with product, engineering, and data teams on model-backed features and experiments
• Contributing to decisions around success metrics, trade-offs, and rollout quality
• Helping connect ML work to real customer-facing outcomes rather than isolated technical milestones
• Bringing technical clarity into cross-functional planning and prioritization

You Might Be a Fit If…

You like shipping real systems

You enjoy moving beyond notebooks and prototypes into production systems that users depend on.

You’re comfortable with language-heavy problems

You like working with text, ambiguity, and the messiness of real-world language data.

You care about quality and reliability

You understand that applied AI work is not just about generating outputs — it’s about making them usable, measurable, and dependable.

You think in trade-offs

You know that model quality, latency, cost, and product usefulness often need to be balanced carefully.

You work well across functions

You’re comfortable partnering with engineers, product managers, and other stakeholders to turn technical work into product value.

Likely Requirements

We expect this role to be best suited to someone with:

• Experience building and shipping machine learning systems in production
• Strong Python skills and comfort with modern ML and data tooling
• Experience working with NLP, LLMs, or other language-focused machine learning systems
• Comfort with SQL and data workflows involving large-scale structured or unstructured data
• Experience designing evaluation and monitoring approaches for ML outputs
• Strong communication skills across technical and non-technical stakeholders
• The ability to work independently in a remote role across the USA or Europe

Strong Signals

These are not strict requirements, but they would stand out:

• Experience with retrieval-based systems, RAG workflows, or grounded generation
• Experience building prompt libraries, orchestration layers, or model-backed content/analysis systems
• Experience with experimentation around model quality, cost, and latency
• Familiarity with observability and monitoring for production ML systems
• Experience in SaaS, AI, analytics, search, SEO, or digital visibility-related products
• Experience working in an early-stage environment with high ownership

Why This Role Matters

Ansvisor is building in a category where unstructured information, AI-generated outputs, and changing discovery behaviors all need to be interpreted at scale. That requires more than standard analytics. It requires well-designed ML systems that can turn noisy language data into clear, useful intelligence.

The person in this role would help build that layer. Their work would influence how Ansvisor processes data, how product capabilities evolve, and how effectively the company uses applied AI to create customer value.

This is an opportunity to work on production AI systems in a category where the underlying problems are still taking shape.

Role Status

This is an upcoming role, not an actively open position today. We’re sharing it because we expect it to become an important hire as the company grows, and we’d like to hear from people who may be a strong fit when the time comes.

Location: Remote
Regions: USA or Europe

How to Express Interest

If this role sounds aligned with your background, feel free to send us:

• Your CV or LinkedIn profile
• A short note on why this role interests you
• Optional: examples of ML systems, NLP/LLM workflows, evaluations, or production features you’ve built or helped ship

Send details to: career@ansvisor.com

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