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About This Role
*Federal Project - Applicant must be a United States Citizen with the ability to obtain a Public Trust*
Baer is looking for Generative AI Application Engineer for a 7-month Federal remote Project.
Title: Generative AI Application Engineer
Location: Remote (Must be based in US)
Duration: 7 months (December Start)
Rate: All-inclusive
Alignment: W2 or C2C
Overview
We are seeking a Generative AI Application Engineer with 2–5 years of experience to develop and integrate enterprise AI solutions using RAG, prompt engineering, vector search, and LLM orchestration. The role focuses on building secure, scalable, production-ready AI applications in collaboration with cross-functional teams. Experience with OCI Generative AI, Oracle APEX, and Oracle Database is preferred.
Description:
- Develop and integrate RAG pipelines, embeddings, vector search, and hybrid retrieval capabilities.
- Implement prompt templates, grounding strategies, guardrails, and LLM orchestration workflows.
- Integrate LLM services with enterprise applications using REST APIs, backend microservices, and PL/SQL callouts.
- Implement validation logic, fallback mechanisms, secure error handling, and responsible AI controls.
- Integrate vector databases and search capabilities, preferably with Oracle Database or Autonomous Database.
- Collaborate with data engineering teams to access structured data, understand schemas, and optimize AI data retrieval.
- Develop evaluation datasets and support retrieval quality assessments, grounding accuracy checks, and prompt regression testing.
- Collaborate with DevOps and cloud teams on deployment pipelines, monitoring, configuration, secrets management, and cost optimization.
- Support automated testing, logging, auditing, security controls, and production readiness for AI-enabled features.
- Participate in Agile ceremonies and collaborate with architects, developers, QA engineers, and business analysts.
- Maintain technical documentation, reusable design patterns, and operational runbooks.
Requirements:
- 2–5 years of software engineering experience in application development or backend engineering.
- Hands-on experience developing Generative AI solutions using RAG, prompt engineering, LLM orchestration, and evaluation techniques.
- Experience integrating LLM APIs such as OCI Generative AI, OpenAI, Azure OpenAI, or Anthropic.
- Proficiency in Python or Node.js for backend development and API integrations.
- Familiarity with SQL, relational databases, structured data, vector databases, and vector search.
- Understanding of RAG evaluation, retrieval precision/recall, grounding accuracy, automated prompt testing, and guardrails.
- Experience with enterprise application integration, API gateways, security, access controls, and secrets management.
- Preferred experience with OCI, Oracle APEX, Oracle Autonomous Database, or Oracle Database 23c vector search.
- Familiarity with AI monitoring, observability, audit logging, and responsible AI practices.
- Strong problem-solving, communication, collaboration, and technical documentation skills, including reusable design patterns and operational runbooks.
Public Trust Security Clearance is the lowest level of additional background screening that the federal government requires for applicants of certain jobs, which includes completing a Standard Form 85 (SF85) form.
Company Overview:
Baer provides best-in-class engagement experiences for our consultants. Our job requirements are carefully vetted and are typically associated with pivotal programs offering tremendous opportunities to expand your skills leveraging the latest solutions.
Baer is an equal opportunity employer including disability/veteran.
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 The Baer Group, 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.
The Baer Group AI Hiring
The Baer Group has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Denver, CO, US, Herndon, VA, US.
Location Context
AI roles in Denver pay a median of $200,000 across 143 tracked positions. That's 6% 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
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