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About This Role
Position Overview
We are seeking a talented AI Engineer to design, develop, and deploy AI-powered applications using modern Artificial Intelligence and Generative AI technologies. The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and Python-based application development.
The candidate will work with engineering and business teams to build intelligent applications, integrate AI capabilities into existing systems, and deliver scalable, reliable solutions for real-world business problems.
Key Responsibilities
Design, develop, and maintain AI-powered applications using Python and modern AI frameworks.
Integrate Large Language Models (LLMs), including GPT, Claude, Llama, or similar models, into enterprise applications.
Build and optimize RAG pipelines using embeddings, vector databases, semantic search, and document retrieval.
Develop AI agents and intelligent workflows using frameworks such as LangChain, LangGraph, or similar technologies.
Implement prompt engineering, tool calling, and model evaluation techniques to improve AI response quality and reliability.
Develop REST APIs and backend services to integrate AI solutions with enterprise applications, databases, and external systems.
Work with structured and unstructured data to support AI applications, knowledge retrieval, and model inference.
Deploy and maintain AI applications on cloud platforms such as AWS or Microsoft Azure.
Test, troubleshoot, and optimize AI applications for accuracy, performance, scalability, and cost efficiency.
Implement logging, monitoring, security controls, and appropriate safeguards for production AI systems.
Collaborate with software engineers, data engineers, and business stakeholders to translate requirements into practical AI solutions.
Required Qualifications
Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience.
3+ years of relevant software engineering, AI/ML, or application development experience.
Strong programming skills in Python and experience building backend applications or APIs.
Hands-on experience with LLMs, Generative AI, or machine learning applications.
Understanding of RAG architectures, embeddings, semantic search, and vector databases.
Experience with at least one AI development framework, such as LangChain, LangGraph, or LlamaIndex.
Experience developing REST APIs using FastAPI, Flask, or similar frameworks.
Familiarity with cloud platforms, preferably AWS or Azure.
Knowledge of Git, software testing, debugging, and collaborative development practices.
Strong analytical, problem-solving, and communication skills.
Preferred Qualifications
Experience building AI agents, agentic workflows, or multi-step AI automation.
Familiarity with Model Context Protocol (MCP), tool calling, and agent orchestration.
Experience with AWS Bedrock, Amazon SageMaker, Azure OpenAI, or Azure AI Foundry.
Familiarity with vector databases such as FAISS, Pinecone, or ChromaDB.
Knowledge of machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
Experience with Docker, Kubernetes, CI/CD, or MLOps/LLMOps practices.
Familiarity with SQL, data pipelines, Apache Spark, or PySpark.
Experience with Microsoft Copilot Studio, Power Automate, or conversational AI solutions.
Knowledge of AI evaluation, hallucination mitigation, data privacy, and responsible AI practices.
What We're Looking For
Mid-Level Engineers: Strong hands-on development skills, a solid understanding of LLMs and RAG, and the ability to build and maintain AI applications with appropriate guidance.
Senior Engineers: Deeper experience designing production AI solutions, optimizing RAG and agent workflows, making technical decisions, and supporting deployment and performance improvements.
Candidates do not need to have experience with every technology listed. Strong Python development skills and practical experience building AI applications are the primary priorities.
Pay: From $85,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Health savings account
- Paid time off
- Vision insurance
Experience:
- Copilot Studio: 4 years (Required)
- Contact Center: 4 years (Required)
Work Location: Hybrid remote in Sterling, VA 20166
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 Antra, 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.
Antra, Inc AI Hiring
Antra, Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sterling, VA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 roles, $210,000 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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