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About This Role
Company Description
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Job Description
About the Role
We are looking for a Senior Software Engineer – AI Engineer to design, build, and productionize AI-powered solutions that solve real-world business and customer problems. You will work across machine learning, generative AI, LLMs, backend engineering, and cloud technologies to build scalable, reliable, and secure AI applications.
The ideal candidate is a strong software engineer who is comfortable working with AI/ML technologies and can take an AI solution from experimentation and prototyping through production deployment and ongoing optimization.
Key Responsibilities
- Design and develop scalable AI-powered applications and services.
- Build and integrate Generative AI and Large Language Model (LLM) capabilities into production applications.
- Develop AI/ML pipelines for data preparation, model training, evaluation, deployment, and monitoring.
- Work with LLMs, embeddings, vector databases, Retrieval-Augmented Generation (RAG), prompt engineering, and AI agents.
- Design and implement APIs and backend services that integrate AI capabilities with existing products and platforms.
- Evaluate different models and approaches based on accuracy, latency, cost, scalability, and reliability.
- Develop strategies for prompt optimization, model evaluation, hallucination reduction, and response quality.
- Build production-ready solutions with appropriate observability, logging, monitoring, security, and failure handling.
- Collaborate with product managers, software engineers, data scientists, and other stakeholders to translate business requirements into technical solutions.
- Participate in architecture and design discussions and contribute to technical decisions.
- Perform code reviews and help establish engineering best practices.
- Mentor other engineers and contribute to the technical growth of the team.
- Stay current with developments in AI, machine learning, LLMs, and related technologies and identify opportunities to apply them to products.
Technical Skills
Programming: Python, Java, SQL
AI/ML: Machine Learning, Generative AI, LLMs, NLP, embeddings, RAG, prompt engineering, model evaluation
AI Frameworks: LangChain, LlamaIndex, PyTorch, TensorFlow, or equivalent
Backend: REST APIs, microservices, distributed systems
Cloud: AWS / Azure / Google Cloud
Databases: SQL and NoSQL databases, vector databases
DevOps: Git, CI/CD, Docker, Kubernetes, monitoring and observability
What You'll Bring
- Strong problem-solving and analytical skills.
- Ability to turn ambiguous problems into practical technical solutions.
- Strong ownership and ability to drive projects from concept to production.
- A product-oriented mindset with a focus on measurable outcomes.
- Ability to balance experimentation with production-quality engineering.
- Strong communication and collaboration skills.
- Curiosity and willingness to continuously learn and experiment with emerging AI technologies.
Role Details
Job Title: Senior Software Engineer – AI Engineer
Experience: 5+ years
Employment Type: Full-time
Department: Engineering / Artificial Intelligence
Qualifications
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
- 5+ years of professional software engineering experience, with experience building and deploying production systems.
- Strong programming skills in Python and/or Java.
- Strong understanding of software engineering principles, object-oriented design, data structures, algorithms, and system design.
- Hands-on experience with Generative AI, LLMs, or machine learning systems.
- Experience working with APIs and integrating AI/ML models into applications.
- Experience with cloud platforms such as AWS, Azure, or Google Cloud.
- Experience with databases and data processing systems.
- Strong understanding of testing, debugging, CI/CD, version control, and production operations.
Preferred Qualifications
- Experience with OpenAI, Anthropic, Gemini, or other foundation models.
- Experience building RAG-based applications using embeddings and vector databases.
- Experience with vector databases such as Pinecone, Weaviate, Milvus, OpenSearch, or pgvector.
- Experience with frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Experience developing AI agents and tool-using LLM applications.
- Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
- Experience with model evaluation, fine-tuning, or parameter-efficient fine-tuning.
- Familiarity with MLOps/LLMOps practices.
- Experience with Docker, Kubernetes, and cloud-native application development.
- Knowledge of AI security, data privacy, responsible AI, and model safety practices.
- Experience designing highly scalable distributed systems.
Additional Information
All your information will be kept confidential according to EEO guidelines.
Salary Context
This $50K-$600K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $193K across 172 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 9,653 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Converge Squad 2, 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 $220,000 based on 1,448 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($325K) sits 48% above the category median. Disclosed range: $50K to $600K.
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.
Converge Squad 2 AI Hiring
Converge Squad 2 has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Randolph, NJ, US. Compensation range: $600K - $600K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 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).
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 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.
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