AI/ML Engineer- Dallas, TX
At a glance
Mid-level Machine Learning Engineering role at Fa Ertb Saasfaprod1. United States · $47,000–$166,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build ML pipelines from incoming data and feature preparation through model evaluation and production release
- Apply classical ML, deep learning and LLM methods to problems involving structured and unstructured data
- Add LLM features such as retrieval-augmented generation, agents and automated workflows to applications
- Set up model versioning, automated delivery, ongoing training, performance monitoring and drift checks
What you bring
- You have strong Python skills, including practical work with NumPy, Pandas and Scikit-learn
- You have built production ML systems using supervised, unsupervised and deep learning methods
- You bring hands-on experience with LLM frameworks, prompt design, retrieval-augmented generation and vector search
- You understand system design and scalable software practices, and have delivered production systems beyond prototypes
Who this fits
This mid-level engineering role suits someone who can take AI and ML work from design through deployment and ongoing monitoring. It is for a full-time employee, not an independent contractor. The stated annual salary range is $47,000 to $166,000, with compensation based on experience and qualifications.
From the employer
We are seeking a highly experienced AI/ML Engineer with strong Python expertise to drive the design and deployment of scalable machine learning and AI systems. In this role, you will architect end-to-end AI/ML solutions, combining classical machine learning, deep learning, and modern LLM-based approaches to solve complex business problems.
You will go beyond experimentation and prototype development to build production-grade AI systems that are reliable, scalable, and continuously improving through feedback loops and monitoring.
The ideal candidate brings deep expertise in ML system design, strong engineering discipline, and the ability to integrate cutting-edge AI capabilities (including LLMs and Agentic systems) into enterprise-grade applications.
Key Responsibilities End-to-End ML System Development
Design, develop, and deploy scalable ML models and pipelines—from data ingestion and feature engineering to model training, evaluation, and production deployment.
Advanced AI Solutioning
Apply a combination of classical ML, deep learning, and LLM-based techniques to solve structured and unstructured data problems across domains.
LLM & Agentic Integration
Build and integrate LLM-powered features, including RAG pipelines, intelligent agents, and workflow automation using modern frameworks.
Model Lifecycle Management (MLOps)
Establish robust MLOps practices including versioning, CI/CD for models, continuous training, monitoring, and drift detection.
Technical Requirements Core ML Expertise
Strong experience across supervised, unsupervised, and deep learning techniques with hands-on implementation in production environments.
Programming & Frameworks
- Expert-level Python (NumPy, Pandas, Scikit-learn)
- Deep learning frameworks: TensorFlow / PyTorch
- Experience in building APIs (FastAPI/Flask)
LLMs & Modern AI Stack
- Hands-on experience with LLM frameworks (LangChain, LlamaIndex)
- Understanding of prompt engineering, RAG, and vector search systems
- Familiarity with OpenAI, Gemini, Anthropic models or open-source alternatives
Data & Infrastructure
- Experience with big data tools (Spark, Hadoop)
- Strong SQL skills and working knowledge of data warehouses
- Experience with vector databases (Pinecone, Weaviate, Milvus, Chroma)
MLOps & Deployment
- Experience with Docker, Kubernetes, CI/CD pipelines
- Model serving frameworks (MLflow, SageMaker, Vertex AI, etc.)
- Monitoring tools for model performance and drift
Software Engineering Best Practices
- Strong understanding of system design, scalability, and clean coding principles
- Experience building production-ready systems (not just prototypes)
Preferred Qualifications
- Experience with Agentic AI systems and orchestration frameworks (LangGraph, CrewAI, AutoGen)
- Knowledge of fine-tuning techniques (LoRA, QLoRA, transfer learning)
- Experience designing real-time ML systems or recommendation engines
- Exposure to multi-modal models (text, image, audio)
- Contributions to open-source projects or strong GitHub portfolio
- Experience mentoring junior engineers and leading technical initiatives
Compensation, Benefits and Duration
Minimum Compensation: USD 47,000
Maximum Compensation: USD 166,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post