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About This Role
Opening for an early career GenAI Application Developer to build and enhance AI\-powered applications for an AWS Contact Center As a Service (CCAS) platform that uses Amazon Connect, Lex V2, Amazon Q in Connect, Contact Lens, Bedrock, OpenSearch, and Lambda (Python). This is a software development role focused on conversation flow design, Amazon Connect integrations, AI service orchestration, generative AI safety, and the creation of supporting APIs and data pipelines to improve customer experience through intelligent automation.
You will work with senior developers, UI/UX designers, and cloud engineers to implement features that deflect customer contacts with voice/chat bots, optimize routing and flows, provide agents with real\-time AI assistance, and ensure privacy/compliance controls. This is an exciting opportunity to grow your development skills while delivering secure, reliable, and innovative solutions in a mission environment.
Primary Responsibilities
- Amazon Connect application development and configuration:
- Build, update, and maintain Amazon Connect contact flows, routing profiles, and queues to support both voice and chat channels.
- Integrate Lex V2 bots into Amazon Connect flows for call deflection, self\-service transactions, and escalation.
- Enable and configure Contact Lens for real\-time and post\-contact analytics, transcription, summarization, sentiment analysis, and redaction.
- Configure Amazon Q in Connect domains, knowledge sources, guided workflows, and step\-by\-step agent assist experiences.
- Implement S3\-based call and chat transcript storage with encryption, lifecycle policies, and retention compliance.
- AI orchestration and backend services:
- Write AWS Lambda (Python) functions to orchestrate Bedrock LLM calls, embeddings workflows, and model invocation logging.
- Implement OpenSearch indexing, vector/keyword queries, and knowledge synchronization triggers.
- Build retrieval\-augmented generation (RAG) pipelines to enhance Amazon Connect agent assist and self\-service knowledge.
- Apply structured logging, unit/integration tests, error handling, and performance/cost safeguards.
- Data safety and compliance:
- Implement AI guardrails, prompt templates, and output evaluation for safety and accuracy.
- Enforce PII minimization and redaction policies in Amazon Connect conversation logs.
- Participate in threat modeling and support remediation of findings for contact center integrations.
- Web and chat integration:
- Support CloudFront \+ WAF configurations for secure web chat entry points.
- Build APIs and event hooks to pass conversation context between web chat, Amazon Connect, and AI services.
- DevSecOps and operations:
- Contribute to Git\-based CI/CD pipelines, code reviews, and documentation.
- Maintain runbooks, architecture diagrams, and SOPs for contact flows, bot integrations, and AI workflows.
- Create and monitor CloudWatch dashboards/alarms for call deflection rate, average handle time (AHT), contact resolution, and AI usage metrics.
Basic Qualifications
- Bachelor’s degree in Computer Science, Engineering, or related field and 2–4 years of relevant experience; or a Master’s with
- 2\+ years of software development with Python, including building and troubleshooting AWS Lambda functions.
- Hands\-on experience with Amazon Connect setup and configuration, including contact flows, routing profiles, queues, and channel integration (voice, chat).
- Experience integrating Lex V2 bots with Amazon Connect flows.
- Familiarity with conversational AI design, including slot elicitation, error recovery, and safe fallback patterns.
- Infrastructure as Code experience (CloudFormation) and Git\-based CI/CD workflows.
- Foundational security knowledge: least privilege IAM, encryption at rest (KMS), and secure logging/monitoring.
- Strong written and verbal communication; able to document designs and explain technical choices to teammates.
- US Citizen with ability to obtain a Public Trust clearance.
Preferred Qualifications
- Practical exposure to full Amazon Connect deployments, including telephony setup, contact attributes, and queue performance optimization.
- Experience enabling and tuning Contact Lens for compliance, sentiment analysis, and post\-contact QA scoring.
- Experience with Amazon Q in Connect for agent assist workflows and knowledge retrieval.
- Experience with Amazon Bedrock (LLM and embeddings) and guardrails; calling LLM APIs and prompt engineering.
- Knowledge of Retrieval Augmented Generation (RAG) and vector search; AWS OpenSearch Service configuration (VPC only, KMS) and k\-NN/HNSW indices.
- Familiarity with KMS key policies, grants, and cross\-account access; S3 data protection (BPA, lifecycle, access points).
- Experience with AWS WAF, CloudFront, and edge security patterns.
- Observability and analytics skills: CloudWatch dashboards/alarms, X\-Ray, Athena/Glue.
- Understanding of privacy/compliance considerations (PII redaction, data retention), and familiarity with FISMA and FedRAMP concepts as applied to contact centers.
- Certifications (any of): AWS Developer Associate, AWS Solutions Architect Associate/Professional, AWS AI Specialty, AWS DevOps Engineer, CISSP/CCSP.
Gridiron offers a comprehensive benefits package including medical, dental, vision insurance, HSA, FSA, 401(k), disability \& ADD insurance, life and pet insurance to eligible employees. Full\-time and part\-time employees working at least 30 hours per week on a regular basis are eligible to participate in Gridiron’s benefits programs.
Gridiron IT Solutions is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status or disability status.
Gridiron IT is a Women Owned Small Business (WOSB) headquartered in the Washington, D.C. area that supports our clients' missions throughout the United States. Gridiron IT specializes in providing comprehensive IT services tailored to meet the needs of federal agencies. Our capabilities include IT Infrastructure \& Cloud Services, Cyber Security, Software Integration \& Development, Data Solution \& AI, and Enterprise Applications. These capabilities are backed by Gridiron IT's experienced workforce and our commitment to ensuring we meet and exceed our clients' expectations.
Pay: $90,000\.00 \- $120,000\.00 per year
Benefits:
- Dental insurance
- Health insurance
- Vision insurance
Work Location: Remote
Salary Context
This $90K-$120K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Gridiron IT, 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. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($105K) sits 37% below the category median. Disclosed range: $90K to $120K.
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.
Gridiron IT AI Hiring
Gridiron IT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $120K - $120K.
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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