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About This Role
AI Engineer — NLP (Conversational Fleet Analytics)
Level: Mid to senior
About the role
V\-Assistant is a conversational AI system running in production on Velocitor's VTrack fleet management platform. Users ask natural\-language questions about vehicles, drivers, safety events, scorecards, and inspections, and get back formatted answers with charts and tables. Under the hood it is a LangGraph tool\-calling agent over 28 domain tools that wrap the VTrack API, fronted by NeMo Guardrails, backed by PostgreSQL with pgvector for retrieval and agent checkpointing, and served to an embeddable React chat widget over an NDJSON stream. It is deployed across five environments on Azure Container Apps.
What you will work on
- Take over and then extend the core chat pipeline: guardrails, conversational query reformulation, embedding\-based tool routing, the LangGraph agent, response formatting, and follow\-up question generation.
- Maintain and add to the domain tool layer over the VTrack API, including argument schemas, authorization checks, pagination, date handling, and error formatting.
- Support the system in production: respond to incidents, investigate latency and quality regressions, and improve the telemetry and runbooks where the current instrumentation makes diagnosis harder than it should be.
- Improve retrieval quality for the RAG\-backed knowledge tools using PostgreSQL full\-text search and pgvector, and help decide where a hybrid approach is warranted.
- Contribute to an evaluation practice that gates model and prompt changes: representative and adversarial datasets, tool\-selection and argument accuracy, shadow traffic, canary rollout, and automated rollback.
- Help reduce and control LLM cost and latency through per\-request token and cost telemetry, prompt and context trimming, caching, model tiering, and elimination of redundant LLM stages.
- Strengthen security boundaries: tenant\-scoped credentials and queries, server\-side tool authorization independent of the model, and prompt\-injection defense across the prompt, retrieval, tool, authorization, and output layers.
- Extend the tiered test strategy across commit, PR, nightly, and release gates
Technical environment
Backend: Python 3\.12, FastAPI, Pydantic v2, SQLAlchemy 2 with Alembic, async psycopg/asyncpg, LangChain and LangGraph, Azure OpenAI via langchain\-openai, NeMo Guardrails, ONNX Runtime embeddings via FastEmbed, LangFuse and structlog for observability, httpx, strict mypy and ruff, pytest with DeepEval.
Frontend: React 19, TypeScript, Vite, Tailwind v4, @assistant\-ui/react for the chat runtime, TanStack Query, Radix UI, Recharts, MSW, Vitest and Testing Library.
Infrastructure: Azure Container Apps, Azure PostgreSQL Flexible Server with pgvector, Front Door, Key Vault, Container Registry, OpenTofu/Terraform across five environments, Azure DevOps Pipelines.
Architecture patterns: domain\-driven design with domain, application, and infrastructure layers; CQRS in the L\&D module; dependency injection container; UI/hook/connector separation on the frontend.
Required qualifications
- Three or more years building and supporting backend services in production, with hands\-on experience shipping at least one LLM\-backed feature that real users depend on.
- Demonstrated ability to take ownership of an existing codebase you did not write, including reading unfamiliar code, using tests and traces to establish how it actually behaves, and making safe changes before you understand every corner of it.
- Strong Python: async programming, type\-driven design, and comfort working in a strict mypy codebase.
- Working experience with an LLM orchestration framework such as LangChain, LangGraph, or an equivalent agent framework, including tool and function calling.
- Solid PostgreSQL skills: schema design, query performance, migrations, and an understanding of connection\-pool behavior under load.
- Experience supporting a live service: diagnosing production issues from telemetry, reasoning about blast radius, and knowing when to roll back rather than fix forward.
- Judgment about when an autonomous agent is appropriate and when a deterministic workflow is the better design, especially for operations that modify data or carry compliance requirements.
- Understanding of security boundaries in AI systems: treating model output and retrieved content as untrusted, enforcing authorization outside the model, and scoping data access per tenant.
- Ability to debug across service boundaries using traces, per\-stage latency metrics, and correlation IDs rather than guesswork.
- Familiarity with retries, backoff with jitter, circuit breakers, and concurrency limits when working against rate\-limited upstream providers.
- Testing discipline that goes beyond unit tests, including contract tests against external APIs and some exposure to evaluating non\-deterministic components.
Nice to have
- Prior experience on a vendor\-to\-in\-house or team\-to\-team handover of a production system.
- Azure experience, particularly Container Apps, OpenAI deployments and quota management, and Key Vault.
- Terraform or OpenTofu, and Azure DevOps Pipelines.
- Vector search and RAG systems at scale, including chunking strategy, hybrid retrieval, and reranking.
- LLM\-as\-judge evaluation, and awareness of its failure modes such as scoring variance, verbosity bias, and susceptibility to injection.
- Guardrails frameworks such as NeMo Guardrails, or equivalent safety\-layer work.
- Modern React and TypeScript, enough to be effective in the widget and admin SPA when a feature spans the stack.
- Data retention and privacy engineering: classification, deletion across messages, traces, embeddings, and caches, legal holds, and third\-party provider retention terms.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Velocitor Solutions, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Velocitor Solutions AI Hiring
Velocitor Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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