ML Engineer – Generative AI / AWS | Richardson, TX | Long-Term Contract

Job Overview

We are seeking an experienced ML Engineer – Generative AI / AWS to join a long-term project in Richardson, Texas. The ideal candidate will have strong expertise in Python, Generative AI, Agentic AI, AWS, backend development, LLMs, RAG, and cloud-native architecture.

This role is suited for a senior engineering professional who can build and deploy scalable AI-powered applications, develop LLM and multi-agent solutions, and integrate machine learning capabilities into enterprise applications.

H1B and U.S. Citizen candidates are accepted on C2C.

Position Details

Job Title: ML Engineer – Generative AI / AWS
Location: Richardson, TX
Experience: 10+ Years
Job Type: Long-Term Contract
Engagement: C2C
Eligibility: H1B & U.S. Citizen C2C
Specialization: Machine Learning / Generative AI / Agentic AI / AWS / Python

Key Responsibilities

• Design, develop, and deploy scalable AI/ML applications using Python and modern machine learning technologies.
• Build production-ready Generative AI and LLM-powered applications.
• Develop backend services and APIs using FastAPI, Flask, Django, and microservices architectures.
• Design and implement RAG pipelines for enterprise AI applications.
• Develop AI agents and multi-agent systems using modern agentic AI frameworks.
• Integrate foundation models such as OpenAI GPT, LLaMA, Hugging Face, or similar models into enterprise applications.
• Develop effective prompt engineering strategies to improve LLM performance and application reliability.
• Implement embeddings, semantic search, and vector database solutions.
• Design cloud-native AI/ML architectures using AWS.
• Develop scalable machine learning workflows and feature engineering pipelines.
• Process large datasets using Apache Spark and PySpark.
• Build ETL and data-processing pipelines to support AI/ML workloads.
• Develop and maintain containerized applications using Docker and Kubernetes.
• Implement Infrastructure as Code using Terraform.
• Develop and maintain CI/CD pipelines for automated application and model deployments.
• Design and implement scalable system architectures using appropriate design patterns.
• Work with SQL and NoSQL databases for application and AI/ML data requirements.
• Monitor application and model performance and troubleshoot production issues.
• Collaborate with Data Scientists, Data Engineers, Software Engineers, Cloud Architects, DevOps teams, and business stakeholders.
• Participate in Agile/Scrum ceremonies and contribute to technical planning and solution design.

Generative AI & LLM

Strong hands-on experience with Generative AI and Large Language Models is required.

The candidate should have experience with:

• LLM application development
• RAG architecture
• LangChain
• LangGraph
• Foundation models
• OpenAI GPT
• LLaMA
• Hugging Face
• Prompt engineering
• Embeddings
• Semantic search
• Vector databases
• LLM evaluation and optimization

The selected engineer will be expected to design AI-powered applications that can reliably integrate enterprise data and business workflows with modern foundation models.

Agentic AI & Multi-Agent Systems

Experience with Agentic AI and multi-agent architectures is highly important.

Responsibilities may include:

• Designing AI agents for business workflows.
• Building multi-agent systems.
• Developing agent tools and integrations.
• Creating agent orchestration workflows.
• Implementing memory and context management.
• Connecting agents with APIs, databases, and enterprise systems.
• Improving agent reliability, accuracy, and scalability.

Python & Backend Development

Strong Python development experience is mandatory.

Experience should include:

• FastAPI
• Flask
• Django
• REST APIs
• Microservices
• Backend architecture
• API integrations
• Asynchronous processing
• Application testing
• Production troubleshooting

The candidate should be capable of developing clean, scalable, secure, and maintainable backend services that support AI/ML applications.

AWS & Cloud-Native Architecture

Strong hands-on AWS Cloud experience is required.

The candidate should have experience designing and deploying cloud-native applications and AI/ML workloads on AWS.

Responsibilities may include:

• Designing scalable AWS architectures.
• Deploying AI/ML applications in cloud environments.
• Managing application infrastructure.
• Implementing cloud security and scalability practices.
• Optimizing application performance and reliability.
• Integrating AWS services with AI/ML applications.
• Supporting production workloads and cloud infrastructure.

Machine Learning & Data Engineering

• Develop machine learning workflows for enterprise applications.
• Perform feature engineering and data preparation.
• Build and maintain ML pipelines.
• Train, evaluate, and optimize machine learning models.
• Process large datasets using Apache Spark and PySpark.
• Develop scalable ETL/data-processing pipelines.
• Support integration between data engineering and ML workflows.

Architecture & System Design

• Design scalable and maintainable AI/ML systems.
• Apply appropriate system architecture and design patterns.
• Develop highly available and fault-tolerant services.
• Evaluate technical approaches and recommend suitable architectures.
• Document technical designs, APIs, integrations, and deployment processes.
• Work closely with architects and engineering teams on enterprise solution design.

DevOps & Infrastructure

Hands-on experience with:

• Docker
• Kubernetes
• Terraform
• CI/CD
• Git
• Automated deployment
• Infrastructure as Code

The candidate should be comfortable deploying and maintaining AI/ML applications across development, testing, and production environments.

Database Experience

Experience working with both SQL and NoSQL databases.

The candidate should understand:

• Data modeling
• Query optimization
• Application data storage
• Vector data storage
• Database integrations
• Data retrieval for AI/ML applications

Required Qualifications

• 7+ years of ML/Software Engineering experience.
• Strong Python and backend development experience.
• Hands-on experience with FastAPI, Flask, Django, and microservices.
• Strong Generative AI and LLM experience.
• Experience with RAG, LangChain, and LangGraph.
• Experience with foundation models such as OpenAI GPT, LLaMA, or Hugging Face.
• Experience building Agentic AI or multi-agent systems.
• Strong prompt engineering, embeddings, semantic search, and vector database knowledge.
• Strong AWS and cloud-native architecture experience.
• Experience with Apache Spark and PySpark.
• Machine learning workflow and feature engineering experience.
• Strong system architecture and design skills.
• Experience with Docker, Kubernetes, Terraform, and CI/CD.
• SQL and NoSQL database experience.
• Experience working in Agile/Scrum environments.
• Strong communication, analytical, and problem-solving skills.

Ideal Candidate

The ideal candidate is a senior ML Engineer who combines traditional machine learning expertise with modern Generative AI, LLM, Agentic AI, and AWS cloud engineering.

You should be comfortable taking AI solutions from architecture and development through deployment and production support. Strong experience building RAG applications, AI agents, backend microservices, cloud-native platforms, and scalable ML workflows will be particularly valuable.

Location & Contract

📍 Richardson, Texas

Duration: Long-Term Contract
Engagement: C2C
Eligibility: H1B & U.S. Citizen C2C

How to Apply

Qualified candidates with strong Python, Generative AI, Agentic AI, AWS, LLM, RAG, LangChain, LangGraph, and ML engineering experience are encouraged to submit their updated resume.

📩 Share Resume:
nagender@burgeonits.com

Please include your current location, total experience, AI/ML experience, Generative AI/LLM experience, visa/work authorization, and availability when submitting your profile.