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About This Role
Charlotte, NC, United States (Hybrid)
Contract (5 months 18 days)
Published 2 days ago
data integration
AI Platforms
aws cloud
performance optimization
SDK Integration
MLOps \& CI/CD
rest apis
python programming
We are seeking a highly skilled AWS Generative AI Engineer with strong experience in designing developing and deploying Generative AI solutions on AWS The ideal candidate should possess hands\-on expertise with Amazon Bedrock Anthropic Claude models Large Language Models LLMs prompt engineering retrieval augmented generation RAG and scalable cloud native architectures The role involves collaborating with business and technology teams to build AI powered applications that drive innovation and business value
Key Responsibilities:
- Design develop and deploy Generative AI solutions leveraging Amazon Bedrock Anthropic Claude and other foundation models
- Build and optimize RAG Retrieval Augmented Generation architectures using vector databases and enterprise knowledge sources
- Develop AI powered applications such as chatbots virtual assistants document processing content generation and intelligent search solutions
- Create and optimize prompts workflows and model configurations to improve response quality accuracy and performance
- Integrate Bedrock foundation models with enterprise applications APIs and AWS services
- Implement scalable and secure cloud native solutions using AWS services such as Lambda API Gateway ECS EKS S3 DynamoDB and OpenSearch
- Finetune model outputs through prompt engineering guardrails and evaluation frameworks
- Collaborate with data engineers architects product owners and business stakeholders to identify GenAI use cases and deliver solutions
- Monitor model performance costs latency and security compliance
- Ensure adherence to responsible AI practices governance and security standards
- Stay updated with emerging trends in Generative AI LLMs and AWS AIML services
Required Skills Experience:
AWS Cloud:
- Strong experience with AWS Cloud Services
Hands\-on experience with
- Amazon Bedrock
- AWS Lambda
- API Gateway
- Amazon S3
- Amazon DynamoDB
- Amazon OpenSearch
- ECSEKS
- CloudWatch
- IAM
Generative AI LLMs:
- Strong experience with Amazon Bedrock and Anthropic Claude models
Solid understanding of:
- Large Language Models LLMs
- Prompt Engineering
- RAG Architecture
- Vector Databases
- AI Agents and Agentic Workflows
- Finetuning and Model Evaluation
- Responsible AI and AI Governance
Programming Frameworks:
- Proficiency in Python
Experience with:
- LangChain
- LangGraph
- LlamaIndex
- FastAPI
- REST APIs
- SDK integrations
Data Integration:
- Experience integrating structured and unstructured data sources
- Knowledge of embeddings semantic search knowledge bases and document ingestion pipelines
Preferred Qualifications:
- Bachelors or Masters degree in Computer Science Engineering Data Science or a related field
AWS certifications such as:
- AWS Certified
- AWS Certified Machine Learning Engineer
- AWS AI Practitioner
- Experience with MLOps CICD and infrastructureascode tools Terraform CloudFormation
- Familiarity with other LLM providers such as OpenAI Meta Llama Mistral or Cohere
Experience:
- 5\-10 years of overall software cloud development experience
- 2\-4 years of hands\-on experience in Generative AI and LLM based solution development
- Proven experience delivering enterprise scale AI solutions on AWS
Key Competencies:
- Solution Design Architecture
- Generative AI LLM Expertise
- CloudNative Development
- Problem Solving Analytical Thinking
- Stakeholder Management
- Innovation Mindset
- Agile Delivery
- Communication and Collaboration Skills
Keywords:
- AWS Amazon Bedrock Anthropic Claude Generative AI GenAI LLM RAG LangChain LangGraph LlamaIndex Vector Database Prompt Engineering Python AI Agents OpenSearch Lambda Cloud Architecture
The pay range that the employer in good faith reasonably expects to pay for this position is $30\.74/hour \- $48\.04/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 $62K-$99K 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 Capgemini, 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 ($81K) sits 62% below the category median. Disclosed range: $62K to $99K.
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.
Capgemini AI Hiring
Capgemini has 20 open AI roles right now. They're hiring across AI Architect, Prompt Engineer, AI/ML Engineer, Data Engineer. Positions span Chicago, IL, US, Alpharetta, GA, US, New York, NY, US. Compensation range: $65K - $191K.
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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