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About This Role
Benefits:
- Competitive salary
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
About the Role
We are looking for a passionate and hands-on AI Engineer to design and develop AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, and modern machine learning frameworks. The ideal candidate has strong Python fundamentals, practical project experience, and the ability to turn emerging AI capabilities into reliable product prototypes and production-ready services.
Key Responsibilities
- Develop AI-powered applications and services using Python and modern AI/ML frameworks.
- Build and integrate LLM-based solutions using OpenAI, Llama, Hugging Face, or similar foundation models and platforms.
- Develop RAG applications using embeddings, vector databases, document chunking, retrieval, reranking, and grounding techniques.
- Build agentic AI workflows using LangChain, LangGraph, tool/function calling, memory, and orchestration patterns.
- Develop REST APIs and lightweight backend services using FastAPI, Flask, or similar frameworks.
- Support prompt engineering, model/application evaluation, hallucination analysis, debugging, and performance optimization.
- Optimize token usage, prompt efficiency, context-window management, caching, latency, and LLM API costs while maintaining application quality.
- Prepare and transform structured and unstructured data for AI experimentation and product use cases.
- Use Git, Docker, automated testing, and CI/CD practices to support repeatable AI engineering workflows.
- Collaborate with product, cloud, data, QA, and solution architecture teams throughout the development lifecycle.
- Maintain technical documentation and evaluate new AI models, frameworks, and approaches for enterprise use cases.
Required Skills
- 1–4 years of professional, internship, research, or advanced project experience in AI/ML, Generative AI, or software engineering.
- Strong programming skills in Python.
- Solid understanding of Artificial Intelligence, Machine Learning, and Deep Learning fundamentals.
- Good understanding of Generative AI, LLM architectures, prompt engineering, embeddings, and vectorization.
- Hands-on project experience with OpenAI, Llama, Hugging Face, or similar models and platforms.
- Experience or strong exposure to LangChain, LangGraph, RAG, vector databases, and Agentic AI concepts.
- Experience with token usage optimization, prompt efficiency, context-window management, caching, latency, and LLM API cost optimization.
- Familiarity with System One decision models.
- Basic experience building APIs with FastAPI, Flask, or similar Python frameworks.
- Working knowledge of Git, Docker, SQL, pandas/NumPy, and software engineering practices.
- Strong analytical, debugging, logical, and communication skills.
Mandatory Project Experience
Candidates should demonstrate at least 2–3 hands-on AI/GenAI projects involving one or more of the following:
- LLM-based applications or AI assistants.
- RAG solutions and vector database implementations.
- Agentic AI workflows or tool-using agents.
- Prompt engineering and structured AI evaluation.
- Machine learning models using scikit-learn, PyTorch, or TensorFlow.
- FastAPI/Flask-based AI services.
- AI solutions solving a real-world business or operational problem.
Preferred Skills
- Exposure to Azure AI, Azure Machine Learning, AWS Bedrock, SageMaker, or equivalent cloud AI services.
- Experience with Pinecone, ChromaDB, FAISS, Qdrant, Milvus, pgvector, or similar vector databases.
- Exposure to Computer Vision, NLP, intelligent document processing, conversational analytics, or enterprise analytics.
- Experience applying token optimization techniques to improve the efficiency and cost of LLM-based applications.
- Experience with System One design.
- Experience with AI explainability and approaches that help users understand model outputs and application behavior.
- Familiarity with Docker, CI/CD, Linux, cloud deployment, or MLOps practices.
Educational Qualification
- Bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Information Technology, Computer Engineering, or a related engineering discipline.
- Candidates with strong academic performance and hands-on AI projects will be preferred.
What We're Looking For
- Strong passion for AI and emerging technologies with a builder mindset.
- Ability to learn quickly, experiment, and convert concepts into working software.
- Strong ownership, curiosity, and willingness to test and improve your own work.
- Ability to work effectively in a fast-paced product engineering environment.
About Novacis Digital
Novacis Digital is an AI-focused technology company developing intelligent solutions for government and commercial organizations. Our work spans intelligent document processing, analytics and insights, case management, conversational AI, and AI agents that support complex business workflows. We bring together product engineering, cloud technology, and industry expertise to turn emerging AI capabilities into practical solutions. At Novacis Digital, you will collaborate with a growing team, contribute to real-world AI applications, and help shape products that improve how organizations process information, make decisions, and deliver services.
Flexible work from home options available.
Salary Context
This $75K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →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 Novacis Digital, 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. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($97K) sits 54% below the category median. Disclosed range: $75K to $120K.
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.
Novacis Digital AI Hiring
Novacis Digital has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Rockville, MD, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 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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