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About This Role
AI Software Engineer – Agentic AI & Python
Location: Jersey City, NJ — Onsite
Annual Base Salary: $85,000–$120,000
Build What’s Next at Globant
We are seeking an AI Software Engineer to build secure, production-ready AI agents and digital copilot capabilities. In this role, you will combine software engineering, data engineering, and applied AI to deliver intelligent workflows for research, summarization, and follow-up activities.
You will work across agent orchestration, enterprise integrations, retrieval pipelines, model delivery, and user-facing experiences, with a focus on security, reliability, and responsible AI.
What You Will Do
- Design, develop, and troubleshoot agent components, including orchestration logic, tools, and task queues supporting research, summarization, and follow-up workflows.
- Build and maintain Model Context Protocol (MCP) servers that provide secure access to CRM systems, client and product reference data, document stores, calendars, and email, enforcing user entitlements on every call.
- Write secure, high-quality production code in Python, and review, debug, and improve code written by others.
- Develop retrieval pipelines for product, pricing, legal, and client documents, including ingestion, chunking, embedding, indexing, and citation metadata to ensure answers are traceable to their sources.
- Build data pipelines and feature engineering jobs on Databricks to prepare CRM, transaction, and engagement data for model training and copilot retrieval.
- Contribute to MLOps workflows for propensity, next-best-action, and opportunity-scoring models, including training jobs, automated testing, and promotion of model artifacts from development through user acceptance testing (UAT) to production.
- Develop the copilot’s user-facing experience and streaming interactions using AG-UI, including human-in-the-loop review before communications are sent to clients or changes are written to CRM.
- Create agent evaluations, including offline test sets, groundedness and citation checks, CI/CD regression suites, and LLM-as-judge scoring.
- Instrument services with OpenTelemetry tracing to support observability and troubleshooting.
- Use enterprise-authorized AI-assisted development tools while validating generated outputs for correctness, performance, and security.
- Identify opportunities to eliminate recurring issues or automate remediation to improve the operational stability of agents and model services.
- Contribute to a team culture rooted in diversity, opportunity, inclusion, and respect.
What You Bring
- Formal training or certification in software engineering concepts and at least 3 years of applied experience.
- Hands-on experience in system design, application development, testing, and operational stability.
- Proficiency in Python and at least one additional language, such as Java, TypeScript, or SQL.
- Experience building and consuming APIs and event-driven services in a cloud environment.
- Experience with data processing frameworks such as Apache Spark and large structured datasets.
- Working knowledge of LLM application development, including prompting, tool calling, retrieval-augmented generation (RAG), and evaluation.
- Experience using approved AI-assisted software development tools, with sound judgment when validating their outputs.
- Understanding of responsible AI practices, data sensitivity, and secure handling of inputs and outputs.
- Solid understanding of agile delivery practices, CI/CD, application resiliency, and security.
- Practical cloud-native experience, including containers and Kubernetes; AWS experience is preferred.
Nice to Have
- Experience with agent frameworks such as Google ADK or LangGraph and protocols such as MCP, A2A, or AG-UI.
- Experience with vector search and retrieval systems such as OpenSearch, pgvector, or FAISS, including reranking and document parsing.
- Experience with Databricks, MLflow, and Delta Lake or Apache Iceberg.
- Exposure to model serving on Kubernetes and monitoring for drift, latency, and accuracy.
- Familiarity with Salesforce or other CRM platforms, including their APIs and data models.
- Front-end development experience with TypeScript and React for conversational or assistant interfaces.
- Exposure to payments, treasury services, commercial cards, or sales enablement processes.
- Familiarity with Terraform and infrastructure as code.
Ready to Make an Impact?
Join Globant and help build secure, reliable AI-powered experiences that connect enterprise data with meaningful business workflows.
Salary Context
This $85K-$120K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $194K across 269 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 11,520 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Globant, 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 1,688 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($102K) sits 53% below the category median. Disclosed range: $85K to $120K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Globant AI Hiring
Globant has 1 open AI role right now. They're hiring across AI Software Engineer. Based in NJ, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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