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About This Role
Dallas, TX, United States (On-site) Contract (9 months 5 days) Published 22 hours ago Databricks Platform cloud platforms Docker & Kubernetes data modeling mlops DevOps & CI/CD PySpark/Python
- We are seeking a highly skilled Data Engineer with strong expertise in Databricks, Python, and PyTorch to design, develop, and optimize scalable data and machine learning pipelines. The ideal candidate will be responsible for building cloud-native data platforms, enabling advanced analytics, and supporting AI/ML initiatives through robust data engineering practices.
- This role requires hands-on experience in large-scale data processing, feature engineering, model deployment support, and performance optimization across modern data ecosystems.
Key Responsibilities:
Data Engineering & Platform Development:
- Design, develop, and maintain scalable data pipelines using Databricks, PySpark, and Python.
- Build and optimize batch and real-time data ingestion frameworks from diverse data sources.
- Develop ETL/ELT solutions to support enterprise data warehousing, analytics, and AI/ML workloads.
- Implement data quality checks, validation frameworks, monitoring, and observability solutions.
- Optimize Databricks jobs, notebooks, clusters, and workflows for performance and cost efficiency.
- Design and maintain Delta Lake architectures, data models, and metadata management solutions.
Machine Learning Data Enablement:
- Collaborate with Data Scientists and ML Engineers to prepare training datasets and feature stores.
- Support development and deployment of deep learning solutions using PyTorch.
- Develop scalable feature engineering pipelines and data transformation frameworks.
- Enable model training, experimentation, and inference infrastructure on Databricks.
- Integrate ML workflows with MLOps pipelines and CI/CD processes.
Cloud & DevOps:
- Implement DataOps and MLOps best practices.
- Automate deployments using CI/CD pipelines.
- Work with cloud-native services for storage, security, monitoring, and orchestration.
- Support infrastructure-as-code and environment automation initiatives.
Collaboration & Governance:
- Partner with business, analytics, and data science teams to understand requirements and translate them into technical solutions.
- Ensure compliance with data governance, privacy, and security standards.
- Participate in architecture reviews and technology evaluations.
Required Skills:
Technical Skills:
- Strong experience with Databricks Platform
- Expertise in Python
- Strong hands-on experience with PySpark
- Experience with PyTorch for machine learning and deep learning workloads
- Knowledge of Delta Lake, Unity Catalog, and Databricks Workflows
- Experience with ETL/ELT pipeline development
- Strong SQL and data modeling skills
- Experience with distributed computing and big data architectures
- Knowledge of REST APIs and data integration patterns
- Git version control and CI/CD tools
Cloud Platforms:
Experience with one or more:
- Microsoft Azure (preferred)
- AWS
- Google Cloud Platform (GCP)
Data Technologies:
- Spark
- Kafka/Event Streaming
- Azure Data Factory or equivalent integration tools
- Data Lake architectures
- Data Warehousing solutions
- Feature Stores
Preferred Skills:
- Experience with GenAI and LLM data pipelines.
- Knowledge of MLflow and MLOps frameworks.
- Hands-on experience with Docker and Kubernetes.
- Experience with real-time streaming architectures.
- Familiarity with vector databases and AI platforms.
- Certifications in Databricks or Cloud platforms.
Qualifications:
- Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or related field.
- 5+ years of experience in Data Engineering.
- 3+ years of experience with Databricks and cloud-based data platforms.
- Experience supporting machine learning and AI use cases using PyTorch.
Nice-to-Have Experience:
- Retail, QSR, Restaurant Technology, Digital Commerce, or Customer Analytics domains.
- Large-scale enterprise data modernization initiatives.
- AI/ML and advanced analytics implementations.
The pay range that the employer in good faith reasonably expects to pay for this position is $39.30/hour - $61.40/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.
Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.
Salary Context
This $81K-$126K range is in the lower quartile for AI Agent Developer roles in our dataset (median: $175K across 28 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 9,079 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Capgemini, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $215,000 based on 203 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($104K) sits 52% below the category median. Disclosed range: $81K to $126K.
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.
Capgemini AI Hiring
Capgemini has 32 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer, Data Scientist, AI Agent Developer. Positions span Atlanta, GA, US, Dallas, TX, US, Conshohocken, PA, US. Compensation range: $85K - $400K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
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).
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
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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