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About This Role
Title: Gen AI Developer
Location: Woodlawn, MD
Duration: Long Term
Experience: 15\+ YearsMOI: 1*st* round video call \& 2*nd* round Face to Face
Key Required Skills: This role will play a critical role in implementing a proof of concept in the production environment and will needs to be a hands\-on developer. The ideal candidate is passionate about understanding and experimenting with AI technologies that benefit society while minimizing risks. Position Description: Collaborate with stakeholders to understand and refine customer\-provided use cases for Generative AI solutions. Design, develop, and implement end\-to\-end Proofs of Concept (PoCs) using Azure AI and AWS Bedrock platforms. Build and maintain scalable, secure, and robust web applications, integrating Generative AI models and APIs. Develop both front\-end and back\-end components, ensuring seamless user experience and efficient data processing. Rapidly prototype and iterate on application features based on feedback and evolving requirements. Integrate cloud services and manage deployment pipelines for PoC applications. Document technical designs, development processes, and application architecture for knowledge sharing and future reference. Collaborate with cross\-functional teams, including data scientists, UI/UX designers, and project managers, to deliver high\-quality solutions. Conduct code reviews, testing, and debugging to ensure application reliability and performance. Stay current with emerging technologies and best practices in Generative AI and full stack development. Skills Requirements: 15\+ years of experience in full stack development, including both front\-end and back\-end technologies. 12\+ years of experience in Python development. You should be comfortable writing clean, efficient code. Hands\-on experience developing applications using cloud platforms such as Microsoft Azure and/or Amazon Web Services. Familiarity with Generative AI concepts and experience integrating AI/ML models or APIs into applications. Proficiency in programming languages such as Python, JavaScript (Node.js, React, or Angular), or similar technologies. Experience with RESTful APIs, microservices architecture, and containerization (e.g., OpenShift or Docker). Strong understanding of software development best practices, version control (e.g., Bitbucket), and agile methodologies. Excellent problem\-solving skills and ability to work collaboratively in a team environment. Clear and effective communication skills are necessary for collaborating with team members, presenting findings, and explaining complex AI concepts to non\-technical stakeholders. Proficiency in utilizing Microsoft Azure services, with a focus on AI and ML services such as Azure OpenAI, Azure AI Search, and Azure Vision. Understanding of fundamental AI and RAG concepts for developing generative AI applications. Commitment to ethical AI development, ensuring adherence to principles like fairness, transparency, accountability, and privacy in AI applications. Proficient in Python and familiar with current best practices and recent language features. Experience with Python web frameworks for building APIs and backend services. Strong experience in implementing and consuming RESTful web services. Solid experience with software development best practices, including unit testing, continuous integration with tools like Jenkins, and version control with Bitbucket. Experience with containerization and orchestration tools like Docker and OpenShift will be beneficial for deployment and scaling applications. Familiarity with Azure DevOps for automating builds, testing, and deployment processes within Azure. Understanding of compliance and security best practices within Azure, especially concerning handling sensitive data such as personal disability information. Familiarity with the Azure OpenAI API and its capabilities for natural language processing (NLP) and generative modeling is highly desirable. Mastery of Azure AI services beyond the basics, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Synapse Analytics, would make you a valuable asset. This includes understanding how to leverage these services in combination with Azure OpenAI frameworks for enhanced functionality and scalability. Ability to preprocess, clean, and manipulate data for RAG ingestion. Hands\-on experience deploying generative AI models into production environments on Azure infrastructure is highly desirable. Understanding deployment considerations such as containerization, orchestration, monitoring, and security ensures smooth integration of AI solutions into real\-world applications. Proficiency in C\# and Java.
Pay: $80\.00 \- $81\.00 per hour
Work Location: In person
Salary Context
This $166K-$168K range is below the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At BN Associates, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($167K) sits 24% below the category median. Disclosed range: $166K to $168K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BN Associates AI Hiring
BN Associates has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Woodlawn, MD, US. Compensation range: $168K - $168K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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