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About This Role
At Technatomy, we deliver innovative solutions through the efforts of our diverse and talented people who are dedicated to our customer’s success. We provide solutions to agencies and entities including the Department of Veterans Affairs, Department of Defense, Defense Logistics Agency, National Institute of Health, and more. Everything we do is built on a commitment to do the right thing for our customers, our people, and our community. Our Mission, Vision, and Values guide the way we do business.
If this sounds like an environment where you can thrive, keep reading!
We are seeking an experienced Pega Data / Integration Engineer with AI\-enablement experience to build governed data and service integrations supporting anomaly detection, predictive analytics, intelligent workflow, and configurable report generation for an enterprise Department of Veterans Affairs solution. This is an engineering and integration role—not a pure data\-science position—and is suited to candidates who can connect approved AI services to secure, traceable enterprise data and Pega workflows.
DUTIES AND RESPONSIBILITIES:
- Design and implement data pipelines, services, and integration patterns that support Pega workflows, AI\-assisted reporting, anomaly detection, prediction, and document analysis.
- Integrate approved AI/ML, NLP, or large\-language\-model services through secure APIs or modular connectors without creating unnecessary provider lock\-in.
- Prepare and govern structured and unstructured data, including ingestion, normalization, metadata, lineage, quality scoring, source linkage, and access controls.
- Support retrieval, grounding, citation, prompt/input preparation, output parsing, and mapping of AI results into structured Pega data and workflows.
- Develop reusable data features and services for financial variance, execution trends, workflow delays, compliance checks, narrative quality, and related decision\-support use cases.
- Implement versioning and auditability for models or services, prompts, templates, input fingerprints, confidence scores, explanations, and user dispositions.
- Collaborate with architects, workflow specialists, business SMEs, security personnel, and testers to maintain human\-in\-the\-loop controls, explainability, privacy, and appropriate use.
- Support test\-data preparation and validation of accuracy, bias, drift, false positives/negatives, performance, and operational usefulness before and after release.
- Troubleshoot AI data flows, integration failures, data\-quality issues, latency, and output\-mapping defects across environments.
- Maintain interface, data, model\-integration, configuration, test, and operational documentation in approved repositories.
KNOWLEDGE AND SKILLS REQUIRED:
- 5\+ years of experience in data engineering, enterprise integration, API development, analytics engineering, or comparable technical roles.
- Hands\-on experience integrating AI/ML, NLP, analytics, or model\-based services into an enterprise application or workflow; extensive model\-training research experience is not required.
- Strong knowledge of REST APIs, JSON, authentication, data mapping, error handling, logging, and secure service integration.
- Experience with SQL and at least one programming or scripting language commonly used for data or integration work, such as Java or Python.
- Experience with ETL/ELT, data pipelines, data quality, metadata, lineage, reconciliation, and structured or unstructured data processing.
- Understanding of human\-in\-the\-loop AI, explainability, versioning, monitoring, auditability, and responsible data handling.
- Ability to work with architects, data scientists or vendors, developers, security teams, testers, and business SMEs.
- Strong analytical, troubleshooting, documentation, and communication skills.
KNOWLEDGE AND SKILLS DESIRED:
- Experience with Pega data/integration capabilities, Pega Process AI, Pega GenAI, or another workflow platform’s AI services.
- Familiarity with retrieval\-augmented generation, vector search, document extraction, model endpoints, or cloud AI services.
- Experience in Federal, financial, acquisition, health, or another regulated environment; direct VA experience is not required.
- Working knowledge of FedRAMP, Zero Trust, privacy, ethical AI, bias testing, model monitoring, or AI governance.
- Relevant Pega, cloud, data engineering, integration, or AI certification is preferred, but not required.
EDUCATION:
- Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, or a related discipline, or an equivalent combination of education and experience.
CLEARANCE:
- Must be able to obtain and maintain a Public Trust clearance.
WORK LOCATION:
- Remote
As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.
This position requires U.S. citizenship or Green card.
This position is contingent upon contract award.
Technatomy Corporation is an Equal Opportunity Employer. It is the policy of Technatomy Corporation to afford equal employment opportunity regardless of race, color, religion, national origin, sex, age, marital status, disability or veteran status, or any other status protected by applicable law.
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 Technatomy Corporation, 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.
Technatomy Corporation AI Hiring
Technatomy Corporation has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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