Role: AI Engineer
Location: Dallas, TX ( 5 Days Onsite a Week from Day1 ) – Looking for Local Candidates
Duration: Long Term
Experience: 15 years - MUST
Skill:
- Data Scientist i.e Statistical algorithms/ML models/AWS Sage Maker
- Gen AI i.e LLMs/Open AI/AWS Bed Rock
- AWS - AI Ops using either Sage Maker or Bed Rock
Role Overview
We are seeking a highly skilled AI Engineer to design, develop, and deploy scalable machine learning and AI-driven solutions. The ideal candidate will have strong experience in building production-grade ML systems, working with Large Language Models (LLMs), and delivering high-impact applications in a cloud-based environment.
Key technical skills for Agentic AI in 2026 involve building autonomous, multi-step workflows using Python, LLM orchestration frameworks (LangChain, AutoGen), and API integration. Core competencies include prompt engineering, agentic RAG for data retrieval, planning/reasoning capabilities, and developing robust memory management.
Required Skills & Qualifications
- 5+ years of software development experience in one or more languages: Python (preferred), C/C++, Go, or Java
- 3+ years of experience designing, building, and deploying production ML systems
- Hands-on experience with LLMs including API integration, prompt engineering, fine-tuning, and RAG architectures
- Familiarity with leading LLMs such as OpenAI, Gemini, Llama, Qwen, and Claude
- Strong understanding of machine learning concepts, applied statistics, algorithms, and data structures
- Experience building data pipelines and handling large-scale datasets
- Strong problem-solving skills, ownership mindset, and ability to work in a fast-paced environment
- Excellent communication skills with the ability to explain complex concepts clearly
Preferred Qualifications
- Experience working with AWS cloud services (ECS/EKS, Lambda, S3, DynamoDB, Redshift, SageMaker)
- Knowledge of containerization and orchestration (Docker, Kubernetes)
- Experience with workflow orchestration (Step Functions)
- Familiarity with Infrastructure as Code tools such as Terraform or CloudFormation.
Key Technical Skills & Usage Examples
- Programming (Python): Essential for writing logic, connecting APIs, and building end-to-end workflows.
- Frameworks (LangChain, AutoGen, CrewAI): Used for developing autonomous agent teams and multi-agent orchestration.
- Prompt Engineering & Instruction Design: Creating structured prompts to improve task success rates by up to 35%.
- Planning & Reasoning: Designing agents that break down complex goals into actionable sub-tasks.
- Tool Use & API Orchestration: Connecting AI models to external tools (databases, browsers, calculators) to act upon data, increasing success by 46%.
- Vector Databases & RAG: Implementing retrieval-augmented generation for memory and knowledge management.
- Evaluation & Testing: Testing agent reliability, accuracy, and monitoring for hallucination
Key Responsibilities
- Design, develop, and maintain scalable AI/ML applications, with a strong focus on Python-based systems
- Architect, build, and deploy production ML systems including model serving, evaluation, monitoring, and data pipelines
- Develop and implement solutions using Large Language Models (LLMs), including prompt engineering, fine-tuning, and RAG-based applications
- Integrate LLM APIs and build intelligent applications using vector databases, tool-based agents, and function calling
- Collaborate with cross-functional teams to translate business requirements into scalable AI solutions
- Ensure performance, scalability, and reliability of deployed AI systems
- Continuously evaluate and adopt emerging AI/ML technologies and best practices
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