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About This Role
Description:
About Us:
eSimplicity is a modern digital services company that partners with government agencies to improve the lives and protect the well\-being of all Americans, from veterans and service members to children, families, and seniors. Our engineers, designers, and strategists cut through complexity to create intuitive products and services that equip federal agencies with solutions to courageously transform today for a better tomorrow.
Responsibilities:
The Agentic AI \& MCP Specialist will architect, develop, and operationalize next\-generation agentic systems powered by advanced LLMs and Model Context Protocol (MCP) frameworks. This role focuses on building intelligent, multi\-step, tool\-using agents that can autonomously reason, plan, and execute complex workflows across a cloud\-based analytics ecosystem. The specialist will design and implement agent orchestration frameworks, integrate model\-driven decision logic, and build robust, production\-grade agent capabilities that safely leverage emerging AI techniques.
This position requires a deeply skilled software developer who combines strong engineering fundamentals with hands\-on experience creating agentic systems, working with MCP\-based integrations, designing LLM\-driven tools, and building secure, scalable AI applications. The role provides technical leadership, explores cutting\-edge agentic patterns, drives proof\-of\-concept innovation, and partners with engineering and product teams to translate experimental architectures into real\-world impact.
Requirements:
Required Qualifications:
- All candidates must pass public trust clearance through the U.S. Federal Government. This requires candidates to either be U.S. citizens or pass clearance through the Foreign National Government System which will require that candidates have lived within the United States for at least 3 out of the previous 5 years, have a valid and non\-expired passport from their country of birth and appropriate VISA/work permit documentation.
- Bachelor’s Degree and 10\+ years of software engineering experience
- Experience designing, developing, and supporting production applications, platforms, or services.
- Experience developing agentic AI solutions, including planning, tool utilization, workflow orchestration, multi\-step reasoning, or autonomous task execution.
- Experience designing and implementing Model Context Protocol (MCP) integrations, tool interfaces, or model\-driven service architectures.
- Ability to analyze business, customer, or mission requirements and develop scalable AI\-driven solutions that align with technical and operational objectives.
- Experience with large language model (LLM) development practices, including fine\-tuning, retrieval\-augmented generation (RAG), prompt engineering, and agent interaction patterns.
- Proficiency in Python and experience working with APIs, microservices, distributed computing environments, and cloud\-native architectures.
- Experience deploying and integrating AI agents or LLM\-enabled applications within cloud environments such as Azure, AWS, or Google Cloud Platform (GCP).
- Knowledge of MLOps and LLMOps practices, including model versioning, automated testing, deployment automation, monitoring, performance evaluation, and governance.
- Ability to contribute to solution design discussions, provide technical guidance to team members, and communicate AI\-related concepts to technical and non\-technical audiences.
- Experience using version control systems and CI/CD practices, including source code management, automated testing, deployment pipelines, and release management for production environments.
Desired Qualifications:
- Experience building multi\-agent systems, agent swarms, or coordinated reasoning frameworks.
- Familiarity with advanced tool\-calling strategies, including dynamic tool selection, function\-call planning, or graph\-structured task planners.
- Experience with structured LLM evaluation methods, agent benchmarking, or test harnesses for autonomous systems.
- Knowledge of performance optimization techniques for LLMs and agents, including caching, model distillation, model routing, or accelerated inference.
- Background integrating agentic components with large\-scale data or analytics platforms (e.g., Databricks, Snowflake, Spark).
- Hands\-on experience developing innovative POCs or experimental agentic architectures in fast\-paced R\&D environments.
- Familiarity with emerging agentic frameworks such as Strands Agents, LangGraph, CrewAI, etc.
- Exposure to safety\-oriented design patterns for autonomous systems, including guardrails, validation layers, or constrained\-action frameworks.
- Experience designing and building secure, compliance\-aware systems that handle sensitive data in accordance with HIPAA and federal security standards, including implementation of encryption, access controls, auditability, and governance for protected health information (PHI) within AI/LLM workflows.
Working Environment:
eSimplicity supports a remote work environment operating within the Eastern time zone so we can work with and respond to our government clients. Expected hours are 9:00 AM to 5:00 PM Eastern unless otherwise directed by manager.
Occasional travel for training and project meetings. It is estimated to be less than 5% per year.
Benefits:
eSimplicity offers a comprehensive benefits package, including medical, dental, and vision coverage, 401(k) retirement benefits, paid time off, paid holidays, life and disability insurance, and additional wellness and employee support programs. Eligibility may vary based on employment status and applicable plan terms.
Reasonable Accommodation:
eSimplicity is committed to providing reasonable accommodations to qualified individuals with disabilities during the application and hiring process. Applicants who need assistance or an accommodation should contact Human Resources.
Equal Employment Opportunity:
eSimplicity is an Equal Opportunity Employer, including disability and protected veteran status. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran status, disability, or any othe
Salary Context
This $143K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Esimplicity, 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($171K) sits 20% below the category median. Disclosed range: $143K to $200K.
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.
Esimplicity AI Hiring
Esimplicity has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Columbia, MD, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 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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