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About This Role
Frederick, Maryland 21701 Posted March 29th, 2026
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Job Type: Full Time
Job Category: IT
Job Description
Role: Gen AI Technical LeadLocation: Frederick, MD /Remote
FTE only Job DescriptionRole Overview
We are seeking an experienced AI Technical Lead to design, build, and scale AI systems with a strong focus on Generative AI, Agentic AI architectures, and robust software engineering best practices. This role involves driving end\-to\-end AI solution development—from model selection and fine tuning to system design, deployment, and operationalization.
You will lead technical strategy, mentor engineering teams, and work closely with product, architecture, and business stakeholders to deliver AI\-driven capabilities across the enterprise. Key Responsibilities1\. AI Engineering \& Solution Delivery* Lead the design and development of Generative AI solutions using LLMs, multimodal models, and diffusion models.
- Build and maintain Agentic AI systems, including autonomous agents, tools integration, reasoning frameworks, and multi\-agent workflows.
- Develop production\-grade AI components using strong engineering principles: modularity, maintainability, observability, and testing.
- Optimize models for performance, latency, and cost using techniques like quantization, distillation, and retrieval augmentation.
2\. Software Design* Design scalable, secure, and compliant AI solutions aligned with enterprise engineering standards.
- Ensure adherence to best practices in software development, version control, CI/CD, containerization, and LLMOps/MLOps.
3\. GenAI \& Agentic AI Focus* Lead development of applications using LLMs (OpenAI, Azure OpenAI, Anthropic, Llama, etc.).
- Build RAG pipelines, vector\-based retrieval systems, and knowledge grounding solutions.
- Create autonomous and semi\-autonomous AI agents capable of task execution, planning, and reasoning.
- Drive experimentation with new GenAI techniques—prompt engineering, tool use, function calling, fine\-tuning, and model fine\-tuning.
- Evaluate emerging frameworks: LangChain, Semantic Kernel, AutoGen, etc.
4\. Leadership \& Collaboration* Lead a team of AI engineers and developers; provide mentoring, coaching, and technical guidance.
- Translate business requirements into technical actionable engineering plans.
- Partner with product teams to define AI capabilities, roadmaps, and delivery milestones.
- Evangelize AI best practices and contribute to AI governance, ethics, and responsible AI frameworks.
Required Qualifications* 10\+ years of software engineering experience; 3\+ years in applied AI/ML engineering.
- Strong proficiency in Python, AI frameworks (PyTorch, TensorFlow), and API\-driven development.
- Hands\-on expertise with GenAI, LLM orchestration frameworks, RAG pipelines, and vector databases (Pinecone, FAISS, Azure AI Search).
- Experience developing Agentic AI using tools/functions integration, planning agents, workflows, or multi agent systems.
- Deep understanding of cloud platforms: Preferably Azure, including ML Ops and containerization (Docker, Kubernetes).
- Solid understanding of system design, distributed computing, microservices, and API architecture.
Preferred Skills* Experience fine tuning LLMs.
- Knowledge of multimodal models (vision, speech, text).
- Familiarity with Azure OpenAI, OpenAI, Anthropic Claude, and open\-source models (Llama, Mistral, Gemma).
- Practical knowledge of model evaluation metrics, safety guardrails, prompt optimization, and responsible AI.
- Background in Insurance is a plus.
Soft Skills* Excellent communication skills.
- Strong leadership abilities with experience managing technical teams.
- A problem\-solving mindset with a focus on innovation, quality, and delivery.
- Ability to work in dynamic, cross\-functional environments
Required Skills
CLOUD DEVELOPER
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Realign, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Realign AI Hiring
Realign has 54 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Architect, Data Scientist. Positions span Pennington, NJ, US, MN, US, Stamford, CT, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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