Enterprise Generative AI Engineer (Lead Mid-Level)

Remote
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AI Summary

Design, build, and deploy production-grade AI solutions across multiple industry verticals. Collaborate with business stakeholders and cross-functional teams to deliver scalable AI solutions. Work on cutting-edge Generative AI and LLM applications.

Key Highlights
AI Solution Development
ML Engineering Pipelines
Infrastructure MLOps
Key Responsibilities
Design and implement LLM-powered applications
Build and optimize ML/data pipelines
Implement containerization and CI/CD pipelines
Technical Skills Required
Python PyTorch TensorFlow Kubernetes Docker AWS GCP Azure LLM APIs OpenAI Anthropic Llama Mistral
Benefits & Perks
Flexible, fully remote engagement
Flexible based on project scope
Nice to Have
Experience with MLOps tools
Background in NLP, computer vision, or time-series forecasting

Job Description


Enterprise Generative AI Engineers (Lead Mid-Level)

Location: Remote

Engagement Type: Contract (Offshore/Nearshore)

Team Size: 23 (1 Lead + 12 Engineers)

Duration: Flexible based on project scope

About The Role

We are seeking a small, high-impact team of Enterprise Generative AI Engineers to design, build, and deploy production-grade AI solutions across multiple industry verticals. This role is ideal for professionals experienced in delivering end-to-end AI systems from prototyping to scalable deployment.

The Team Will Include

  • A Senior Lead responsible for architecture, technical direction, and stakeholder engagement
  • Mid-Level Engineers focused on hands-on development and implementation

You will work in a fast-paced, cross-functional environment, collaborating with business stakeholders, product teams, and engineering groups to deliver scalable AI solutions.

Key Responsibilities

AI Solution Development

  • Design and implement LLM-powered applications such as:
    • Retrieval-Augmented Generation (RAG) pipelines
    • AI agents and multi-agent systems
    • Chatbots and generative AI integrations
  • Integrate with commercial and open-source LLMs (OpenAI, Anthropic, Llama, Mistral)
ML Engineering Pipelines

  • Build and optimize ML/data pipelines for:
    • Training, fine-tuning, and evaluation
    • Embeddings and vector search workflows
  • Develop scalable and reusable AI/ML components
Infrastructure MLOps

  • Architect and manage production-grade AI infrastructure
  • Implement:
    • Containerization (Docker, Kubernetes)
    • CI/CD pipelines for ML workflows
    • Model monitoring, logging, and scaling strategies
Stakeholder Collaboration

  • Engage with business stakeholders to:
    • Understand requirements
    • Identify AI use cases
    • Translate ambiguous needs into technical solutions
  • Collaborate with cross-functional teams to align AI solutions with business goals
Best Practices Documentation

  • Establish standards for:
    • Prompt engineering
    • Model evaluation
    • Responsible AI practices
  • Document system architecture, workflows, and operational runbooks
Required Qualifications

Senior Lead (7+ Years Experience)

  • Proven experience delivering enterprise-scale AI/ML systems in production
  • Strong expertise in:
    • Python, PyTorch/TensorFlow
    • Modern ML/AI frameworks and ecosystems
  • Hands-on experience with cloud platforms (AWS, GCP, or Azure)
  • Deep knowledge of:
    • Kubernetes, Docker, and container orchestration
    • MLOps and scalable ML infrastructure
  • Expertise in LLM application patterns :
    • RAG, embeddings, vector databases, function calling
  • Strong leadership and communication skills:
    • Ability to lead architecture decisions
    • Mentor engineers
    • Drive stakeholder discussions and discovery sessions
Mid-Level Engineers (37 Years Experience)

  • Hands-on experience building ML models and data pipelines
  • Proficiency in:
    • Python
    • At least one deep learning framework (PyTorch preferred)
  • Working knowledge of:
    • Cloud platforms (AWS/GCP/Azure)
    • Docker and Kubernetes
  • Familiarity with:
    • LLM APIs and prompt engineering
    • RAG and vector database concepts
  • Ability to:
    • Work independently
    • Translate requirements into high-quality implementations
Nice to Have

  • Experience with MLOps tools (MLflow, Weights Biases, SageMaker, Vertex AI)
  • Background in:
    • NLP, computer vision, or time-series forecasting
  • Experience with:
    • Multi-agent AI systems or orchestration frameworks
    • Vector databases (Pinecone, Weaviate, pgvector)
  • Contributions to open-source AI/ML projects
  • Familiarity with geospatial or remote sensing data
Core Tech Stack

  • Languages Python (primary), JavaScript/TypeScript
  • ML/AI PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex
  • LLM APIs OpenAI, Anthropic, Llama, Mistral
  • Cloud AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (OpenAI Service)
  • Infrastructure Docker, Kubernetes, Terraform, CI/CD
  • Data PostgreSQL, Redis, Vector DBs (Pinecone, Weaviate, pgvector)

Why Join

  • Work on cutting-edge Generative AI and LLM applications
  • Build end-to-end production AI systems
  • Collaborate with experienced professionals in a high-impact, fast-paced environment
  • Flexible, fully remote engagement

This job is provided by Shine.com

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