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About This Role
Location: Austin, United States of America Thales people architect identity management and data protection solutions at the heart of digital security. Business and governments rely on us to bring trust to the billions of digital interactions they have with people. Our technologies and services help banks exchange funds, people cross borders, energy become smarter and much more. More than 30,000 organizations already rely on us to verify the identities of people and things, grant access to digital services, analyze vast quantities of information and encrypt data to make the connected world more secure.Position Summary
Austin, Hybrid
Thales is looking for a Senior AI & Security Automation Engineer who designs, builds, and maintains intelligent automation systems, agentic workflows,
and cloud-native integrations to eliminate operational toil across our Security Services and Operations teams.
Sitting at the intersection of modern software engineering, applied artificial intelligence, and enterprise cybersecurity, this role transforms manual, repetitive workflows—such as log routing, alert enrichment, incident triage, and policy audits—into resilient, automated pipelines, search architectures, and AI copilots that scale service delivery.
Key Areas of Responsibility
1.Applied AI & Agentic Workflow Development
- Architect and deploy multi-step AI agents and autonomous reasoning workflows using Amazon Bedrock and LLM orchestration frameworks to automate alert triage, log summarization, and incident root-cause analysis.
- Build, optimize, and maintain Retrieval-Augmented Generation (RAG) pipelines over internal security runbooks, threat intelligence feeds, and product documentation using Amazon OpenSearch Service and Bedrock Knowledge Bases.
- Implement robust evaluation frameworks, Bedrock Guardrails, and deterministic fallback layers to prevent hallucinations, secure against prompt injections, and ensure operational safety.
2.Security Automation, Search & Integration Engineering
- Develop and maintain event-driven automation pipelines and custom API connectors linking SIEMs, cloud log sources, WAFs, API security platforms, and ticketing systems.
- Design, build, and optimize search and ingestion pipelines to index security telemetry and event logs into Amazon OpenSearch Service for fast querying, anomaly detection, and vector retrieval.
- Build orchestration playbooks to automate routine customer onboarding tasks, configuration drift audits, and security policy deployments.
- Implement detection-as-code and configuration-as-code pipelines using Git version control and CI/CD best practices.
- Internal Tooling & Service Enablement
- Package automation engines and search queries into user-friendly CLI tools, ChatOps bots (Slack/Teams), and internal web portals for security consultants and analysts.
- Partner directly with delivery teams to identify operational bottlenecks and engineer scalable programmatic solutions.
- Monitor pipeline health, Bedrock token consumption, OpenSearch cluster performance/indexing latency, API rate limits, and compute costs across all deployed tools.
- Sales
- Partner with the Sales Engineering team to develop and deliver Value-Added Services (VAS) proof-of-values (POVs), clearly demonstrating the business value and impact of these services.
- Collaborate with Account teams to accelerate time-to-value for Thales solutions among non-VAS customers, strengthening customer adoption, retention, and renewal outcomes.
Minimum Qualifications
- Education: Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related technical field; Master’s degree is a plus.
- Experience: 9+ years of hands-on experience in software engineering, security automation, DevSecOps, or applied machine learning engineering. 4+ years of experience in software engineering, AI/ML, data, cloud, or enterprise technology, including 2+ years designing, developing and delivering AI/ML or GenAI solutions at enterprise scale.
- AWS Cloud & AI Engineering:
o Amazon Bedrock: Hands-on experience developing generative AI applications, agents, and RAG pipelines using Amazon Bedrock (including Bedrock APIs, Knowledge Bases, Guardrails, and Foundation Models such as Anthropic Claude).
o OpenSearch & Vector Search: Demonstrated experience with Amazon OpenSearch Service (or OpenSearch / Elasticsearch) for log analytics, complex querying, index management, and vector database / k-NN search integrations for RAG.
o Serverless & Event-Driven Workflows: Proven experience building automation using AWS Lambda, Amazon EventBridge, AWS Step Functions, and Amazon ECS/Fargate.
o Log Routing & Telemetry: Hands-on proficiency configuring and automating ingestion/export pipelines with Amazon CloudWatch Logs, VPC Flow Logs, and AWS CloudTrail.
o AWS IAM & Security: Deep understanding of least-privilege AWS IAM policies, role assumption (sts:AssumeRole), and credential management via AWS Secrets Manager and KMS.
o Cloud Integration: Working knowledge of asynchronous task routing and ingestion using Amazon API Gateway, Amazon SQS, Amazon SNS, and Amazon S3.
- Programming & Scripting: Advanced proficiency in Python (FastAPI, asyncio, Pydantic, Boto3, OpenSearch-py) and shell scripting (Bash); familiarity with Go is a plus.
- Applied AI Engineering: Proven experience building production workflows using LLM orchestration frameworks (LangChain, LangGraph, or LlamaIndex), semantic search architectures, and structured prompt engineering.
- API & System Integration: Deep working knowledge of RESTful APIs, webhooks, event triggers, JSON/YAML processing, and authentication mechanisms (OAuth2, API keys, mTLS).
- Containers & CI/CD: Hands-on experience with Docker containerization and automated CI/CD pipelines (GitHub Actions, GitLab CI).
- Cybersecurity Fundamentals: Strong understanding of core security domains (WAF, API security, log correlation, incident lifecycle, MITRE ATT&CK).
Applicants must be legally authorized to work in the United States for any employer at the time of hire. This position is not eligible for visa sponsorship or for assuming sponsorship of an employment visa now or in the future.
Preferred Qualifications
- Infrastructure as Code (IaC): Proficiency in provisioning scalable AWS resources (including OpenSearch domains and Bedrock integrations) using Terraform or AWS Cloud Development Kit (CDK).
- Advanced Data & ML Pipelines: Experience with Amazon SageMaker, streaming telemetry via Amazon Kinesis Data Streams / Firehose, or distributed querying with Amazon Athena.
- Native AWS Security Tooling: Working knowledge of AWS WAF, AWS Network Firewall, AWS Security Hub, and Amazon GuardDuty.
- Domain Background: Prior experience building automation or delivery tooling within an enterprise cybersecurity software vendor, SOC, or Managed Security Service Provider (MSSP).
- Industry Certifications:
o AWS Certified Security
o AWS Certified Solutions Architect (Associate or Professional)
o AWS Certified AI Practitioner / Machine Learning Specialty o GIAC AI Security Automation Engineer (GASAE) or equivalent.
Special Position Requirement
Schedule: Monday to Friday 9hrs shift (8hrs work + 1 hour lunch included) and occasional paid weekend on-calls (Once every 2 months)
Why Join Us?
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This position will require successfully completing a post-offer background check. Qualified candidates with [a] criminal history will be considered and are not automatically disqualified, consistent with federal law, state law, and local ordinances.
Thales champions inclusion and we believe diversity strengthens the fabric of our culture. Thales is an Equal Opportunity Employer, including disability/veterans.
If you need an accommodation or assistance in order to apply for a position with Thales, please contact us at [email protected].
The reference Total Target Compensation (TTC) market range for this position, inclusive of annual base salary and the variable compensation target, is between
Total Target Cash (TTC) 127,068.20 - 252,872.90 USD Annual This reflects how companies in a similar industry and geographic region generally pay for similar jobs. This range helps the Company make pay decisions as one data point among many. Where a position falls within this range is also dependent on other factors including – but not limited to – the employee’s career path history, competencies, skills and performance, as well as the company’s annual salary budget, the customer’s program requirements, and the company’s internal equity. Thales may offer additional benefits and other compensation, depending on circumstances not related to an applicant’s status protected by local, state, or federal law.
(For Internal candidate, if you need more information, please raise HR request through MyThales)
Thales provides an extensive benefits program for all full-time employees working 30 or more hours per week and their eligible dependents, including the following:
- Elective Health, Dental, Vision, FSA/HSA, Voluntary Life and AD&D, Whole Group Life w/LTC, Critical Illness, Hospital Indemnity, Accident Insurance, Legal Plan, Identity Theft, and Pet Insurance
- Retirement Savings Plan after 30 days of employment with a company contribution and a match, and with no vesting period
- Company paid holidays and Paid Time Off
- Company provided Life Insurance, AD&D, Disability, Employee Assistance Plan, and Well-being Program
Salary Context
This $127K-$252K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Thales, 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($189K) sits 10% below the category median. Disclosed range: $127K to $252K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Thales AI Hiring
Thales has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Orlando, FL, US, Clarksburg, MD, US, Austin, TX, US. Compensation range: $120K - $252K.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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