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About This Role
Overview
This role supports the engineering, deployment, and operation of internal AI applications built on an established Python and Angular codebase and AWS\-native architecture. The position is focused on production\-quality full\-stack software engineering and cloud deployment of AI\-enabled applications rather than AI research or experimentation.
Responsibilities
The ideal candidate is a strong full\-stack software developer with professional experience in Python back\-end development and Angular front\-end development, along with hands\-on experience working in Git\-based development environments, AWS CI/CD pipelines, Infrastructure as Code (Terraform), and multi\-environment AWS deployments. This individual should be capable of independently developing, testing, reviewing, deploying, and troubleshooting complete application features across the front end, APIs, back\-end services, and AWS infrastructure throughout the software development lifecycle.
AI\-assisted development tools may be used to improve productivity; however, the developer is expected to understand, validate, test, and take ownership of all code they produce. The successful candidate must be able to independently reason through software architecture, application logic, deployment issues, and production failures rather than relying primarily on AI\-generated code.
Technical Responsibilities
Develop, maintain, debug, and enhance production\-quality Python applications and AI services within an established internal codebase
Work effectively within Git\-based development workflows, including branching strategies, pull requests, merge conflict resolution, code reviews, and release management
Build, maintain, and troubleshoot CI/CD pipelines used to deploy applications across AWS development, staging, and production environments
Understand and work within an AWS multi\-account and multi\-environment architecture, including IAM roles, policies, permissions, service accounts, and cross\-service access
Develop and maintain Infrastructure as Code using Terraform, including reviewing Terraform plans and troubleshooting infrastructure deployment failures
Deploy and troubleshoot AWS\-native applications using services such as Lambda, Step Functions, API Gateway, containers, Amazon Bedrock, and OpenSearch
Diagnose deployment and operational issues across application code, CI/CD pipelines, AWS infrastructure, IAM permissions, networking, and service integrations
Implement and extend Retrieval\-Augmented Generation (RAG) and AI orchestration workflows using frameworks such as LangChain and LangGraph
Develop and maintain APIs and data integration services supporting AI applications, structured metadata, and enterprise data sources
Develop and maintain Angular\-based web application interfaces, including components, services, routing, forms, state/data handling, validation, and integration with REST APIs and Python back\-end services
Implement end\-to\-end full\-stack features spanning Angular/TypeScript user interfaces, API contracts, Python services, data access, authentication/authorization, and AWS\-hosted application components
Troubleshoot browser and front\-end issues, API integration failures, application state and data\-flow problems, and cross\-layer defects between the Angular client and Python/AWS services
Write maintainable, testable, and well\-documented code consistent with established software engineering standards
Perform peer code reviews and help establish and enforce coding, testing, source\-control, and deployment standards
Participate in Scrum\-based development, sprint planning, backlog refinement, and technical estimation
Translate business and non\-developer feature requirements into clear technical designs and actionable development work items
Support onboarding and mentoring of developers working within the organization's AI development framework and AWS environment
Collaborate with cloud, cybersecurity, infrastructure, and operations teams to support secure and compliant application deployments
Skills Required
Required Technical Skills
Strong professional Python software development experience, including object\-oriented design, debugging, testing, dependency management, and working within established codebases
Strong professional Angular and TypeScript development experience building and maintaining production web applications within established codebases
Demonstrated full\-stack development experience integrating Angular front ends with REST APIs and Python back\-end services, including data models, validation, error handling, authentication/authorization, and end\-to\-end troubleshooting
Strong working knowledge of modern web application fundamentals, including HTML, CSS, responsive UI development, client\-side routing, asynchronous API communication, and browser\-based debugging
Experience writing and maintaining automated tests for both front\-end and back\-end application components
Strong hands\-on proficiency with Git and Git\-based software development workflows, including branches, pull requests, merges, conflict resolution, tagging, and code reviews
Demonstrated experience building, using, and troubleshooting CI/CD pipelines for automated application deployment
Hands\-on experience deploying and troubleshooting applications in AWS
Working knowledge of AWS IAM, including roles, policies, permissions, and troubleshooting access\-related deployment and runtime issues
Hands\-on experience with Infrastructure as Code, preferably Terraform
Experience working with multiple application environments such as development, staging, and production
Experience developing and integrating REST APIs and CRUD\-based services
Ability to independently troubleshoot issues spanning application code, deployment pipelines, cloud infrastructure, permissions, and service integrations
Experience writing automated tests and validating code before deployment
Experience using AI coding assistants as productivity tools while maintaining independent ownership and understanding of the resulting code
Familiarity with Agile/Scrum software development practices
AI\-Specific Technical Skills
Experience developing production applications using Large Language Models (LLMs)
Hands\-on experience with LangChain and/or LangGraph
Experience implementing or supporting Retrieval\-Augmented Generation (RAG) architectures
Understanding of prompt engineering and structured LLM interactions
Experience integrating applications with hosted LLM platforms; AWS Bedrock experience is strongly preferred
Experience optimizing AI workflows for performance, reliability, accuracy, and cost
Familiarity with vector search, embeddings, metadata filtering, and enterprise knowledge retrieval
Preferred / Nice\-to\-Have
AWS certification or significant equivalent hands\-on AWS experience
Experience with AWS Bedrock, OpenSearch, Lambda, Step Functions, API Gateway, and containerized workloads
Advanced Terraform experience, including reusable modules, state management, and environment\-specific deployments
Experience designing or maintaining CI/CD pipelines and automated deployment processes
Familiarity with AWS CloudWatch and application observability, logging, monitoring, and alerting
Experience with metadata tagging, versioned knowledge sources, and access\-aware retrieval, including RBAC and ABAC concepts
Experience working in secure, regulated, or government cloud environments; AWS GovCloud experience is a plus
Experience participating in technical design reviews, peer code reviews, or mentoring other developers
Experience with additional front\-end frameworks such as React or Blazor, component libraries/design systems, or advanced Angular architecture and performance optimization
What We’re Looking For
5\-7 years experience
We are looking for a hands\-on full\-stack software engineer who specializes in building AI\-enabled applications and can work across an Angular front end, Python back\-end services, APIs, and the AWS infrastructure required to operate those applications in a production environment.
The successful candidate should be comfortable taking a full\-stack feature from requirement through Angular UI implementation, Python/API development, source control, automated testing, code review, CI/CD deployment, and production troubleshooting.
They should be able to navigate an existing codebase and AWS architecture, understand how front\-end, back\-end, data, and cloud components interact, and independently diagnose problems when applications, deployments, or services fail.
This role is best suited for an engineer who uses AI development tools to accelerate their work—not as a substitute for understanding software engineering fundamentals.
We value developers who can explain their design decisions, understand the code they commit, identify and correct issues in AI\-generated code, and take ownership of the reliability and maintainability of the systems they build.
The Ideal Candidate Is
A strong full\-stack software engineer with professional Python and Angular development experience and practical AI application experience
Proficient in Angular and TypeScript and able to independently develop production\-quality user interfaces integrated with Python APIs
Able to own features end to end across the browser, API layer, Python services, data integrations, and AWS deployment environment
Highly proficient with Git and modern collaborative development practices
Comfortable with Terraform, CI/CD pipelines, and AWS deployments
Able to troubleshoot across code, infrastructure, pipelines, and cloud permissions
Comfortable working within an established architecture rather than rebuilding systems from scratch
Focused on maintainable, testable, production\-quality software
Able to balance rapid AI development with security, reliability, and operational requirements
A collaborative team member who can contribute technically while helping other developers become more effective
Salary Range: $75,000 \- $85,000/Year, DOE
Education
A bachelor’s degree in computer science (or other relevant concentration) required. An additional three years of directly relevant software development experience may substitute for the degree requirement.
Full\-Time
person
Entry\-Level
business
On\-site
Benefits
We offer competitive salaries commensurate with education and experience. We have an excellent benefits package that includes:
Health \& Welfare
Company Paid
Major Medical Insurance for employees and family members
Dental Insurance for employees and family members
Vision Insurance for employees (employee\-paid for family members)
Group Life Insurance
Accidental Death and Dismemberment Insurance
Travel Accident Insurance
Long\-Term Disability
Voluntary
Short\-Term Disability
Supplemental Life Insurance
Flexible Spending Account (pre\-tax deferrals for health care expenses)
Retirement
Money Purchase Pension Plan \- 100% Company funded defined contribution retirement plan. One\-year entry waiting period and 5\-year vesting. Core Funds available and a self\-directed brokerage account option.
Employee Stock Ownership Plan \- 100% Company funded discretionary contribution. 100% vested after one\-year entry waiting period.
Time Off
Tecolote’s generous paid time off benefits give employees the flexibility they need to relax, recharge and take care of the unexpected.
Annual Leave
Bereavement Leave
Holidays
Jury Duty/Witness Leave
Military Leave
Sick Leave
Location
Los Angeles Operations
2120 East Grand Avenue, Suite 200
El Segundo, California 90245
(310\) 640\-4700
Tracking Number: 019\-26\-3
Salary Context
This $75K-$85K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tecolote Research, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($80K) sits 63% below the category median. Disclosed range: $75K to $85K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Tecolote Research AI Hiring
Tecolote Research has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in El Segundo, CA, US. Compensation range: $85K - $85K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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