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Digital Walls Consulting

CAREERS

AI Data Analyst

Job Code

D1017

YOE

3 to 5 Years

Type

FTE

Location

Bangalore - Onsite

Apply Now

Key Responsibilities

  • Design, build, and deploy LLM-based applications including RAG pipelines, fine-tuned models, and multi-step agentic workflows
  • Develop and expose AI capabilities as clean APIs using Flask or equivalent frameworks
  • Own problems end-to-end — from scoping and prototyping to production deployment and monitoring
  • Set up and maintain CI/CD pipelines to ensure reliable, repeatable model and service deployments
  • Conduct applied research to evaluate new models, architectures, and techniques relevant to business needs
  • Collaborate with product and business stakeholders to translate requirements into robust AI solutions
  • Write clean, well-tested, and maintainable code that your teammates can build on

Skill Requirements

Core Technical Skills

  • Strong proficiency in Python, including data manipulation, numerical computing, and software design patterns
  • Solid foundations in Data Science and ML — statistics, model evaluation, feature engineering, experiment design
  • Hands-on experience with LLMs (GPT, Claude, Mistral, Llama, etc.) including prompt engineering, context management, and output evaluation
  • Practical experience building RAG systems — chunking strategies, vector stores, retrieval tuning, and grounding
  • Experience with LLM fine-tuning techniques (instruction tuning, LoRA, PEFT) and understanding of when to fine-tune vs. prompt
  • Familiarity with agentic AI patterns — tool-use, memory, orchestration frameworks (LangChain, LlamaIndex, AutoGen, etc.)

Engineering & Deployment

  • Experience building and deploying REST APIs with Flask or similar frameworks
  • Proficiency with CI/CD tools (GitHub Actions, GitLab CI, or similar) and deployment workflows
  • Comfort with cloud platforms (AWS or Azure) for model hosting, storage, and serving
  • Familiarity with containerization (Docker) and basic MLOps practices

Fill your details


    CAREERS

    AI Data Analyst

    Job Code

    D1017

    YOE

    3 to 5 Years

    Type

    FTE

    Location

    Bangalore - Onsite

    Apply Now

    Key Responsibilities

    • Design, build, and deploy LLM-based applications including RAG pipelines, fine-tuned models, and multi-step agentic workflows
    • Develop and expose AI capabilities as clean APIs using Flask or equivalent frameworks
    • Own problems end-to-end — from scoping and prototyping to production deployment and monitoring
    • Set up and maintain CI/CD pipelines to ensure reliable, repeatable model and service deployments
    • Conduct applied research to evaluate new models, architectures, and techniques relevant to business needs
    • Collaborate with product and business stakeholders to translate requirements into robust AI solutions
    • Write clean, well-tested, and maintainable code that your teammates can build on

    Skill Requirements

    Core Technical Skills

    • Strong proficiency in Python, including data manipulation, numerical computing, and software design patterns
    • Solid foundations in Data Science and ML — statistics, model evaluation, feature engineering, experiment design
    • Hands-on experience with LLMs (GPT, Claude, Mistral, Llama, etc.) including prompt engineering, context management, and output evaluation
    • Practical experience building RAG systems — chunking strategies, vector stores, retrieval tuning, and grounding
    • Experience with LLM fine-tuning techniques (instruction tuning, LoRA, PEFT) and understanding of when to fine-tune vs. prompt
    • Familiarity with agentic AI patterns — tool-use, memory, orchestration frameworks (LangChain, LlamaIndex, AutoGen, etc.)

    Engineering & Deployment

    • Experience building and deploying REST APIs with Flask or similar frameworks
    • Proficiency with CI/CD tools (GitHub Actions, GitLab CI, or similar) and deployment workflows
    • Comfort with cloud platforms (AWS or Azure) for model hosting, storage, and serving
    • Familiarity with containerization (Docker) and basic MLOps practices

    Fill your details


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