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About This Role
Senior AI Engineer
Austin, TX
At IPT Associates (IPTA), we enjoy solving real-world problems with technology. We work closely with our customers, teammates, experts, and partners to come up with practical solutions that make a difference. Whether it's a government or business organization, we focus on understanding the need and building something that works.
As a Senior AI Engineer, you are at the heart of our team's AI mission. You will architect, build, and deploy production-grade Generative AI applications, autonomous agent workflows, and retrieval-augmented generation (RAG) systems for Army decision support products.
This is not a traditional ML modeling role, nor is it merely an LLMOps coding position. We require an engineer who possesses a deep theoretical and practical understanding of Foundation Models (FMs), reasoning architectures, and the mechanics underpinning modern AI systems. You will focus on operationalizing FMs, wiring deterministic software systems to non-deterministic LLM reasoning engines, and integrating these nodes directly into data transformation, visualization, and business process pipelines.
Working alongside a dedicated mission team, you will take ownership of the AI architecture and AI Ops for your projects to ensure our systems meet strict operational standards where accuracy is the highest priority, followed by speed, and then cost.
Our Team
At IPTA, everything starts with our people. We're big believers that when you invest in your team, great things happen. That's why we encourage learning, growth, and trying new things-even if you don't have all the answers yet.
Our culture is rooted in fierce determination, fearless integrity, and passionate service-but we also like to keep things collaborative, supportive, and down-to-earth.
We're looking for people who:
- Are curious and excited about technology
- Like solving problems and figuring things out
- Can take initiative but also enjoy working with a team
- Want to keep learning, growing, and challenging themselves
If you're someone who's eager to jump in, learn something new, and make an impact-you'll fit right in here.
Responsibilities:
- Agentic System Architecture: Design and orchestrate multi-step autonomous AI agents primarily using Palantir AIP. Apply deep theoretical knowledge to implement reliable function calling, state machines, and human-in-the-loop (HITL) checkpoints
- TheoreticalApplied Architecture: Serve as the team's intellectual lead on modern AI systems. Translate the underlying theory of attention mechanisms, vector embeddings, and reasoning frameworks into practical, production-ready software architecture
- Deterministic-Stochastic Bridging: Architect resilient stateful backends that safely wrap probabilistic LLM outputs with deterministic validation, retry policies, strict JSON/Pydantic schemas, and structured error handling
- RAGPipeline Integration: Build and scale production RAG pipelines across structured and unstructured Army datasets. Implement hybrid search, advanced chunking, metadata filtering, and re-ranking layers to feed directly into decision support products and visualizations within Foundry
- AI OpsEvaluation (LLMOps): Take complete ownership of project LLMOps. Implement automated regression testing and continuous evaluation pipelines for LLM outputs (e.g., faithfulness, semantic relevancy, hallucination detection) to guarantee deterministic accuracy
- Safety, SecurityGovernance: Enforce guardrails around context boundaries, data isolation, multi-tenant RBAC, PII redaction, and mitigation against prompt injection/model evasion within a secure DoD environment
- PerformanceCost Optimization: Manage context window budgets, token consumption metrics, latency profiling, and model routing strategies, strictly adhering to the priority matrix of Accuracy > Speed > Cost
Requirements:
- Security Clearance: Must hold a current, active DoD Secret clearance
- TheoreticalSystems Depth: Deep theoretical and practical understanding of Generative AI architectures, foundation models, context management, and advanced prompt engineering (e.g., Chain-of-Thought reasoning)
- Enterprise AI Experience: Proven experience building ontology-driven workflows or AI pipelines within Palantir Foundry, Palantir Maven, or other mature production systems. (Note: Extensive Army Vantage experience is not strictly required if you possess strong analogous production experience)
- ProgrammingSystems Integration: 5+ years of experience with advanced proficiency in Python and TypeScript, featuring strong software engineering fundamentals (OOP, microservices, async programming, API design)
- Agentic Framework Proof Points: Strong working knowledge of frameworks such as LangChain and LangGraph. While Palantir AIP is the primary platform, experience with these frameworks serves as a critical proof point of your architectural capabilities and will support future needs
- Vector SearchData Engineering: Proven experience indexing and querying vector databases/extensions (e.g., pgvector, OpenSearch, Pinecone, Qdrant) alongside relational and document datastores
- EvaluationObservability: Practical experience using LLM tracing and evaluation tools (e.g., Langfuse, Arize Phoenix, Ragas, TruLens) to diagnose multi-turn agent failures and latency bottlenecks
Preferred Qualifications:
- Active Top Secret (TS) Clearance
- Experience operating within DoD Impact Level 5/6 (IL5/IL6) secure network environments
- Industry Certifications: Recognized applied AI certifications (e.g., Google Cloud Professional Machine Learning Engineer/Generative AI Developer, Claude Architect, AWS Certified Generative AI Developer - Professional, Microsoft Azure AI Engineer Associate)
- CloudInfrastructure: Demonstrated experience consuming model endpoints over REST/gRPC, deploying asynchronous worker queues, and integrating serverless compute/microservices
IPTA is an Equal Opportunity Employer. All employment decisions are based on individual merit, including qualifications, experience, performance, and business needs, consistent with applicable federal laws and federal contracting requirements. IPTA provides equal opportunity and utilizes merit-based principles to make employment-related decisions.
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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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At IPT Associates, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
IPT Associates AI Hiring
IPT Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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