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About This Role
About the Opportunity
The AI Developer IV will join the Product and Services team as a senior, hands-on developer, designing, building, and integrating production AI solutions, including generative and agentic AI, LLM integrations, RAG and knowledge solutions, APIs, and cloud-based AI services in a remote work arrangement. This role writes production code and takes solutions from design through deployment, turning AI capabilities into reliable, scalable systems that teams and end users can depend on. The ideal candidate brings hands-on expertise in generative AI, LLM integration, and cloud-based AI services, along with the ability to translate emerging AI capabilities into practical, well-engineered solutions.
Success in this role means delivering production-grade AI solutions that run reliably and perform well in real-world use, serving as a trusted partner to stakeholders and users by turning AI ideas into working systems, and strengthening the team's engineering standards through high-quality code, thorough reviews, and sound development practices.
What You Will Do in This Role
The AI Developer IV designs, builds, and deploys production generative and agentic AI solutions, including LLM integrations, RAG pipelines, APIs, and cloud-based AI services, for federal scientific and environmental programs, applying responsible AI practices, monitoring, and continuous quality improvement while collaborating with stakeholders and writing clean, tested code.
Responsibilities include:
- Design, build, and deploy production AI solutions, including generative and agentic AI applications
- Integrate large language models (LLMs) into applications and workflows through APIs and SDKs
- Build RAG and knowledge solutions, including data ingestion, chunking, embeddings, vector search, and retrieval pipelines
- Develop agentic workflows that use tools, orchestration, and multi-step reasoning
- Build and maintain APIs and services that expose AI capabilities to internal teams and downstream systems
- Build AI-driven analytics and automation that improve operational visibility, reporting, and decision-making for federal programs
- Apply AI and LLM solutions to scientific and environmental data, including knowledge retrieval across technical and mission documentation
- Implement responsible AI practices, including human oversight, output validation, and auditability
- Deploy and operate AI services in the cloud using infrastructure as code and CI/CD practices
- Evaluate and improve AI solution quality, including accuracy, latency, cost, and reliability
- Implement monitoring, logging, and guardrails to keep AI systems observable, safe, and trustworthy
- Collaborate with architects, data teams, and stakeholders to translate requirements into working solutions
- Write clean, tested, well-documented code and participate in code reviews
- Perform other duties and responsibilities as assigned.
What You Will Bring
Basic Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience
- 8+ years of professional software development experience, including deploying solutions to production
- Strong proficiency in Python
- Hands-on experience building solutions with LLMs and generative AI (e.g., prompt design, function/tool calling, structured outputs)
- Experience building AI solutions in federal or other regulated environments, with attention to data security, privacy, and compliance (e.g., NIST, FedRAMP)
- Experience building RAG or knowledge-based solutions, including embeddings and vector databases
- Experience building APIs and services (e.g., FastAPI, Flask)
- Hands-on experience deploying applications and AI services in a major cloud environment (AWS, Azure, or GCP)
- Experience with CI/CD tooling and infrastructure as code
- Solid software development fundamentals: testing, version control (Git), code review, and documentation
- Strong communication skills and the ability to collaborate in a cross-functional environment
Preferred Qualifications
- Experience building agentic AI systems with frameworks such as LangChain, LlamaIndex, or similar
- Experience with cloud AI platforms and managed model services (e.g., AWS Bedrock, Azure OpenAI, Vertex AI)
- Experience evaluating and monitoring LLM applications
- Experience with AWS AI services such as Amazon Bedrock and SageMaker
- Experience applying AI to scientific, geospatial, or Earth observation data
- Experience with containerization (Docker) and container orchestration
- Familiarity with classical ML techniques and frameworks (e.g., scikit-learn, PyTorch)
- Cloud or AI/ML certification
Work authorization/security clearance requirements
- Ability to obtain a security clearance.
Work Environment
- This work is normally completed in a remote environment.
Physical Demands
- Prolonged periods of sitting at a desk and working on a computer.
- Must be able to access and navigate each department at the organization's and client facilities.
Travel Required
- No
Proficiency Requirement
- The employee is expected to demonstrate proficiency in all essential job functions, tools, and processes related to this position within the first 90 days of employment. This includes acquiring a thorough understanding of job-specific responsibilities, systems, and workflows as outlined during onboarding and training. Failure to meet this requirement may result in additional training, reassessment, or other actions as deemed necessary by management.
Why GAMA-1
GAMA-1 also offers a variety of benefits, including health insurance coverage, life and disability insurance, 401(k) savings plan, training and career development opportunities, paid holidays and paid time off (PTO - to cover vacation, illness or disability, appointments, emergencies or other situations that require time off from work). For more GAMA-1 Corporate benefits information click here.
ABOUT GAMA-1
GAMA-1 is a rapidly growing technology business that is based in Greenbelt, Maryland. GAMA-1 Technologies provides strategic information assurance, information security, and business enterprise and networking solutions to the Federal Government. Our success is based on using industry and agency standards, establishing and performing with standardized processes, and IT Services expertise. At GAMA-1, we believe employees should grow, achieve, and develop just as the company grows, achieves, and develops. GAMA-1 is committed to providing our employees with opportunities for career advancement throughout their employment. For more information, visit www.gama1tech.com.
GAMA-1 is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to: veteran status, uniformed servicemember status, race, color, religion, sex, sexual orientation, gender identity, age, pregnancy (including childbirth, lactation and related medical conditions), national origin or ancestry, citizenship or immigration status, physical or mental disability, genetic information (including testing and characteristics), domestic violence victims, political orientation, status as a smoker or tobacco user, hairstyle, use of a service animal, education status, familial status, HIV/AIDS status, height, weight, reproductive healthcare decisions or any other category protected by federal, state or local law.
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 Gama-1 Technologies, 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.
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.
Gama-1 Technologies AI Hiring
Gama-1 Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% 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 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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