Interested in this AI/ML Engineer role at VeeRteq Solutions Inc.?
Apply Now →Skills & Technologies
About This Role
Job Description Agentic AI Engineer (Forward Deployment)
Experience: -8+ Years
Location: -3 days work from office
Edina, MN (1st preference)
Chicago, IL (2nd Preference)
Irving, TX (3rd Preference)
What is in it for you?
We are seeking a hands-on Senior Agentic AI Developer / Forward Deployed Engineer to design, build, integrate, and operationalize enterprise applications. The engineer will work closely with business stakeholders, domain experts, product owners, architects, data engineers, quality engineers, and platform teams to translate business problems into secure, testable, and supportable solutions.
This is an embedded delivery role with end-to-end implementation ownership, covering discovery, technical design, application and data development, AI integration, evaluation, deployment, and operational readiness. The successful candidate must be equally comfortable implementing deterministic business rules and developing governed AI-assisted capabilities. This is not solely a prompt-engineering, model-research, or architecture-advisory position.
Roles and Responsibilities:
Embedded Discovery and Solution Delivery: Partner directly with business users and subject-matter experts to understand workflows, pain points, exceptions, and desired outcomes.
Translate business requirements into technical specifications, user stories, testable acceptance criteria, and incremental delivery plans.
Identify when deterministic logic, conventional automation, retrieval, or an AI agent is the appropriate solution.
Build working vertical slices that demonstrate business value rather than disconnected proofs of concept.
Communicate technical trade-offs, risks, dependencies, and decisions clearly to technical and nontechnical stakeholders.
Agentic AI and LLM Application Engineering: Develop bounded AI capabilities for research, explanation, recommendation, and workflow assistance. Implement structured outputs, context retrieval, tool/function calling, prompt versioning, and output validation. Design tool contracts with explicit permissions, schemas, failure behavior, and audit requirements. Ground AI responses in approved, versioned sources and preserve links to supporting evidence. Implement human review, escalation, abstention, and fallback behavior.
Enforce authorization boundaries in application code and tool permissions-not through prompts alone. Evaluate model and framework options against approved enterprise requirements for quality, security, latency, and cost.
Software and Data Engineering: Build production-quality backend services, APIs, data-processing components, and workflow integrations. Develop Databricks notebooks/jobs and SQL transformations for ingestion, normalization, matching, comparison, and validation.
Implement data-quality checks, schema validation, deduplication, reconciliation, and versioned processing rules.mMaintain reproducibility through pinned rule sets, input references, and traceable execution records.
Collaborate on reviewer-facing interfaces and end-to-end application behavior.
Engineer reliable integrations using appropriate retry, idempotency, timeout, and recovery patterns.
Quality, Evaluation, and Verification: Create unit, integration, end-to-end, regression, and negative tests. Develop representative evaluation datasets with domain experts. Assess AI outputs for grounding, correctness, usefulness, safety, and appropriate abstention. Test unauthorized tool use, prompt injection, sensitive-data exposure, and failure scenarios. Trace delivered functionality and verification evidence to acceptance criteria. Review AI-generated code with the same rigor as manually written code.
Security, Governance, and AI-DLC: Follow the applicable AI-DLC lifecycle, approved tooling paths, review requirements, and release controls.
Implement least-privilege access, secure authentication, secrets management, and data-access restrictions.
Use synthetic, mock, or approved de-identified development data where required.
Maintain architecture decisions, prompt/tool documentation, approval evidence, and delivery records.
Escalate unresolved data classification, privacy, model approval, and risk decisions to accountable owners.
Preserve human authority over final business decisions and production promotion.
Operational Readiness: Instrument application behaviour, retrieval, tool calls, model usage, failures, and dependencies.
Establish prompt/model traceability and appropriate telemetry protections.
Implement usage and cost attribution, monitoring, s, and agreed reliability measures.
Produce deployment guidance, rollback procedures, runbooks, and support handover materials.
Work with platform and operations teams while retaining delivery-team accountability for application quality and remediation.Duration: -
Educational Qualifications: -
Engineering Degree BE/ME/BTech/MTech/BSc/MSc.
Technical certification in multiple technologies is desirable.
Skills: -
Mandatory skills
Strong hands-on Python and SQL development skills.
Demonstrated delivery of an LLM-enabled application beyond a basic chatbot or notebook demonstration.
Experience with tool/function calling, structured responses, retrieval-grounded generation, and AI evaluation.
Strong backend/API engineering skills, including validation, authentication, authorization, error handling, and automated testing.
Experience with Databricks or a comparable enterprise data platform, with the ability to work effectively in Databricks.
Understanding of relational databases, data modeling, audit trails, and transactional integrity.
Experience with cloud-hosted applications, preferably on Azure.
Proficiency with Git, code reviews, CI/CD, and reproducible deployment practices.
Ability to diagnose issues across application, data, integration, and infrastructure boundaries.
Strong stakeholder communication and the ability to work independently within a cross-functional delivery team.
Practical understanding of sensitive-data handling and secure enterprise development.
Preferred Qualifications
Experience with FastAPI, React, and TypeScript.
Familiarity with NestJS, Next.js, and PostgreSQL.
Experience with Databricks Apps, Jobs, Delta Lake, Unity Catalog, and asset bundles.
Experience with Azure Blob Storage, managed identities, and application monitoring.
Experience with an agent framework such as LangChain/LangGraph, Azure AI Foundry Agents, or an equivalent framework; willingness to use the approved project stack.
Familiarity with retrieval evaluation, knowledge-base versioning, and reviewer-feedback workflows.
Experience in healthcare data, data-use agreements, compliance-sensitive workflows, or enterprise data validation.
Experience using approved AI coding assistants while maintaining code provenance, testing, and human review.
Skills
RAG Pipeline, AgenticAI, Python, Azure
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At VeeRteq Solutions Inc., 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
VeeRteq Solutions Inc. AI Hiring
VeeRteq Solutions Inc. has 4 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Agent Developer. Positions span Chicago, IL, US, Irving, TX, US, Newtown Sq, PA, US.
Location Context
AI roles in Chicago pay a median of $193,440 across 450 tracked positions. That's 9% below the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.