AI Engineer

Job Title: AI Engineer 

Location: [ Dallas TX , Chalotte NC ,Boston MA ,Danbury CT ]
Client : E Solutions / Persistent Systems
Visa : NO H1 / GC 
Experience: 8 to 10 years

< dir="ltr">Responsibilities

• Design, develop, and maintain ML/AI models and pipelines (training, evaluation, deployment, monitoring)
• Build and integrate LLM-based features (RAG pipelines, agents, prompt engineering, fine-tuning) into production applications
• Collaborate with data engineers to ensure clean, well-structured data pipelines for model training and inference
• Optimize model performance for latency, cost, and accuracy in production environments
• Write clean, well-tested, production-grade code (Python primarily; possibly Go/Java depending on stack)
• Deploy and monitor models using MLOps tooling (CI/CD, model versioning, observability)
• Partner with product managers to translate business requirements into technical solutions
• Participate in code reviews, architecture discussions, and technical design docs
• Stay current with AI/ML research and evaluate new tools, frameworks, and techniques for applicability

< dir="ltr">Required Qualifications

• 8–10 years of experience in software engineering, with 2+ years focused on ML/AI systems
• Strong proficiency in Python and common ML frameworks (PyTorch, TensorFlow, scikit-learn)
• Experience with LLM APIs (OpenAI, Anthropic, etc.) and prompt/agent design
• Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG)
• Solid understanding of data structures, algorithms, and system design
• Experience deploying models to production (Docker, Kubernetes, cloud platforms — AWS/Google Cloud Platform/Azure)
• Familiarity with MLOps practices: experiment tracking, model versioning, CI/CD for ML
• Strong communication skills and ability to work cross-functionally

< dir="ltr">Nice to Have

• Experience fine-tuning or evaluating LLMs
• Familiarity with orchestration frameworks (LangChain, LlamaIndex, custom agent frameworks)
• Experience with distributed computing (Spark, Ray)
• Exposure to A/B testing and experimentation frameworks
• Contributions to open-source ML projects or published research