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    Home/Jobs/Senior Engineer, Applied AI

    Senior Engineer, Applied AI

    Honor

    United States
    5-8 years
    Today
    ₹133–150 LPA
    Full-time
    Remote

    Skills Required

    OpenAI
    Gemini
    Anthropic
    LLM
    RAG
    LangChain
    Agentic AI
    Gen AI
    Embeddings
    Vector Database
    Prompt Engineering
    Fine-tuning
    Python
    Thrift
    MySQL

    Description

    Join Honor Technology as a Senior Engineer on the Applied AI team, helping to apply AI across the operating model, product systems, and internal workflows. The mission is to build durable organizational intelligence and systems that retain operational context and support better decisions. This role involves designing, building, and productionizing foundations for AI-assisted systems.

    Company: Honor

    Role: Senior Engineer, Applied AI

    Location: Remote

    Experience:

    • 5+ years of experience in software engineering, preferably in Python
    • Experience building LLM-powered products, AI agents, retrieval systems, memory systems, or evaluation frameworks
    • Experience working with model providers or platforms such as AWS Bedrock, OpenAI, Gemini, Anthropic, or similar systems
    • Experience with secure data access patterns, RBAC, auditability, or compliant data workflows

    Key Skills:

    • Python
    • Thrift
    • MySQL
    • AWS
    • AWS Bedrock
    • OpenAI
    • Gemini
    • Anthropic
    • JavaScript
    • React
    • Looker
    • GitHub
    • DataDog

    Qualification:

    • Bachelor's degree in Computer Science, Engineering, or related field

    Role Focus:

    • Design, build, and productionize foundations for AI-assisted systems
    • Work closely with engineering, product, design, data, operations, security, and business stakeholders
    • Build systems that power AI-assisted sales follow-up, operational defect analysis, reusable memory and retrieval patterns, internal agentic tools, AI-powered data analysis, schedulable workflows, or closed-loop feedback systems
    • Create secure model access, compliant data pathways, observability, cost controls, evaluation patterns, human-in-the-loop workflows, and reusable architecture

    Additional responsibilities:

    • Participate in on-call rotation in collaboration with tier 1 support team
    • Mentor and spread knowledge to other engineers

    Nice to have:

    • Experience building LLM-powered products, AI agents, retrieval systems, memory systems, or evaluation frameworks
    • Experience working with model providers or platforms such as AWS Bedrock, OpenAI, Gemini, Anthropic, or similar systems
    • Experience with secure data access patterns, RBAC, auditability, or compliant data workflows
    • Experience building internal tools, workflow automation, or systems that support operations teams at scale
    • Experience designing systems that involve human-in-the-loop review, feedback loops, or measurable quality improvement
    • Knowledge of microservice architecture and best practices
    • Experience working with RPC architecture
    • Experience with frontend development and JavaScript frameworks like React
    • Experience leveraging observability and monitoring tools like DataDog

    Other:

    • Base pay: $160,000 - $180,000 USD per year
    • Generous equity packages
    • 401K with up to 4% employer match
    • Medical, dental, and vision coverage
    • Short Term Disability, Long Term Disability, and Life Insurance
    • Generous time off program
    • Mental health benefits
    • Wellness program
    • Discount program

    Prepare for this role

    Recommended resources to build the skills for this position. Sponsored.

    Python for Everybody Specialization

    Coursera

    Learn Python from scratch — variables, data structures, web scraping, and databases.

    Python 3 Programming Specialization

    Coursera

    Intermediate Python covering classes, inheritance, APIs, and data processing.

    Generative AI with Large Language Models

    Coursera

    Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.

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