Lead Gen AI Engineer

CCS INC

Plano (TX)

On-site

USD 150,000 - 210,000

Full time

14 days+
Application generator

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Benefits offered by this job

Bonus based on performance
Dental insurance
Health insurance
Vision insurance

Job summary

A technology company in Plano, Texas seeks an experienced software engineer to design and deploy scalable GenAI applications. This role involves building robust architectures using LLMs, integrating GenAI capabilities into enterprise systems, and implementing responsible AI practices. Candidates should have over 8 years of experience in software engineering, strong programming skills in Python, and familiarity with cloud platforms like AWS. A competitive benefits package is offered including performance bonuses and health insurance.

Qualifications

  • 8+ years of software engineering and development experience.
  • Proven experience in building and deploying GenAI applications in production.
  • Strong programming skills in Python and familiarity with GenAI libraries.
  • Deep understanding of LLMs, embeddings, and vector databases.
  • Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
  • Familiarity with CI/CD for ML workflows and MLflow or DVC.
  • Hands-on experience designing cloud-native solutions (preferably on AWS).
  • Exposure to GenAI tools and frameworks (LLMs, vector DBs, prompt orchestration, LangChain, Bedrock).
  • Familiarity with AWS AI/ML services (SageMaker, Bedrock, Comprehend, Lex).

Responsibilities

  • Design scalable and robust GenAI architectures using LLMs.
  • Integrate GenAI capabilities into enterprise platforms with APIs.
  • Monitor and optimize model performance, latency, and cost.
  • Integrate GenAI capabilities into enterprise platforms using APIs, SDKs, and orchestration tools.
  • Implement responsible AI practices including bias detection, hallucination mitigation, and explainability.
  • Monitor and optimize model performance, latency, and cost.
  • Use techniques like quantization, distillation, and caching to improve efficiency.

Skills

Software engineering
GenAI applications
Programming in Python
LLMs, embeddings, vector databases
Cloud platforms (AWS, Azure, GCP)
Containerization (Docker, Kubernetes)
CI/CD for ML workflows
AI/ML certifications

Education

AWS AI certification

Tools

Transformers
LangChain
Hugging Face
FAISS
Pinecone
Weaviate
MLflow
DVC
SageMaker
Bedrock
SageMaker
Bedrock
Comprehend
Lex

Job description

Benefits:
  • Bonus based on performance
  • Dental insurance
  • Health insurance
  • Vision insurance
Qualifications:
  • 8+ years of software engineering and development experience
  • Proven experience in building and deploying GenAI applications in production.
  • Strong programming skills in Python and familiarity with GenAI libraries (Transformers, LangChain, Hugging Face, etc.).
  • Deep understanding of LLMs, embeddings, vector databases (e.g., FAISS, Pinecone, Weaviate).
  • Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
  • Familiarity with CI/CD for ML workflows and versioning tools like MLflow or DVC.
  • Hands-on experience designing and building cloud-native solutions (preferably on AWS)
  • Exposure to GenAI tools and frameworks (e.g., LLMs, vector databases, prompt orchestration, LangChain, Bedrock)
  • Familiarity with AWS AI/ML services (e.g., SageMaker, Bedrock, Comprehend, Lex)
  • AWS AI certification
Responsibilities
  • Design scalable and robust GenAI architectures using LLMs, multimodal models, and retrieval-augmented generation (RAG).
  • Fine-tune foundation models using domain-specific data.
  • Implement prompt engineering, instruction tuning, and reinforcement learning from human feedback (RLHF).
  • Integrate GenAI capabilities into enterprise platforms using APIs, SDKs, and orchestration tools.
  • Implement responsible AI practices including bias detection, hallucination mitigation, and explainability.
  • Monitor and optimize model performance, latency, and cost.
  • Use techniques like quantization, distillation, and caching to improve efficiency.
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