Senior AI/ML Engineer - R01572335

Brillio 2
Bangalore +1 more
Hybrid

Who this role is best for

Aimed at mid-to-senior level AI/ML engineers who have hands-on experience with LLM frameworks and RAG systems, and are comfortable with production deployment and collaboration with data scientists.

Best fit for

  • Candidates with 2–4 years of experience in LLM-based applications and a strong background in prompt engineering
    — “Experience Range: 2 to 4 years of experience, including at least 2 years specifically focused on developing LLM-based applications”
  • Individuals familiar with RAG pipelines and embedding models, with a focus on scalable and reliable AI solutions
    — “Develop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models, retrieval ranking, and context window management”
  • Engineers who have experience deploying and maintaining AI/ML models in production environments
    — “Deploy, monitor, and maintain AI/ML solutions in production environments, ensuring reliability, scalability, and robust error handling”

Things to consider

  • The role requires a focus on generative AI and LLM systems, which may require specific niche expertise
    — “LLM-based applications, RAG systems, or AI agent workflows”
  • Candidates should be prepared to work closely with data scientists and integrate evaluation frameworks
    — “Collaborate with Data Scientists to instrument solutions for measurement, developing evaluation frameworks”

How to stand out

  • Highlight experience with end-to-end AI agent workflows and production deployment in your resume
    — “Design and build end-to-end AI agent workflows, from initial prompt design through production deployment”
  • Demonstrate systematic testing and refinement of prompts in your interview responses
    — “Building and iterating on prompt engineering layers, systematically testing and refining prompts”
  • Showcase your ability to implement automated quality checks and validation layers
    — “Establish automated quality checks and validation layers to proactively identify and resolve issues”
  • Emphasize your experience with embedding models and vector stores in RAG systems
    — “Develop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models”
  • Mention your work with multi-step agent workflows and tool integration in your application materials
    — “Experience building multi-step agent workflows with tool use and branching logic”
Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Developed and optimized RAG pipelines
  • Implemented feedback loops for agent performance
  • Collaborated with Data Scientists for solution measurement
Typical background
2-4 years of experience in AI/MLAdvanced proficiency in Python

Skills & requirements

Required

PythonLLM FrameworksPrompt EngineeringRAG SystemsAI Agent WorkflowsData Pipeline DevelopmentProduction Deployment

Preferred

Multi-agent Orchestration FrameworksContent GenerationTranslationDocument ProcessingEvaluation FrameworksLLM Cost Optimization

Stack & domain

PythonLangchainLlamaindexPrompt EngineeringRAGPysparkTensorflow Developer CertificateAWS Certified Machine Learning – SpecialtyDeeplearning.ai Generative AI With LlmsAIMLLLM

About the role

Original posting from Brillio 2 via Lever

Senior AI/ML Engineer

Job requirements:

Experience Range: 2 to 4 years of experience, including at least 2 years specifically focused on developing LLM-based applications, RAG systems, or AI agent workflows Key Responsibilities:

  • Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and reliable solutions
  • Develop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models, retrieval ranking, and context window management for precise information retrieval
  • Build and iterate on prompt engineering layers, systematically testing and refining prompts and chain-of-thought strategies to deliver consistent outputs across diverse inputs
  • Implement tool orchestration within agent workflows, integrating agents with databases, rule engines, validation systems, and formatting tools for seamless operation
  • Establish automated quality checks and validation layers to proactively identify and resolve issues before outputs reach human reviewers
  • Collaborate with Data Scientists to instrument solutions for measurement, developing evaluation frameworks and tracking solution performance against defined targets
  • Deploy, monitor, and maintain AI/ML solutions in production environments, ensuring reliability, scalability, and robust error handling
  • Design and implement feedback loops to capture expert review data and translate it into measurable improvements in agent performance

Required Skills:

  • Advanced proficiency in Python
  • Hands-on experience with LLM frameworks such as LangChain or LlamaIndex
  • Expertise in prompt engineering for systematic testing and iteration
  • Deep understanding of RAG architectures including embedding models, vector stores, retrieval strategies, and re-ranking
  • Experience building multi-step agent workflows with tool use and branching logic
  • Experience deploying and maintaining AI/ML solutions in production environments
  • Experience with data pipeline development for feeding AI systems

Preferred Skills:

  • Experience with multi-agent orchestration frameworks
  • Background in content generation, translation, or document processing solutions
  • Familiarity with feedback loops, RLHF, or reward model training
  • Knowledge of multi-modal AI systems including voice-to-text, document understanding, and image analysis
  • Experience with evaluation frameworks for generative AI and automated scoring
  • Experience with LLM cost optimization strategies such as model routing, caching, and prompt compression

Desired Qualifications:

  • Bachelor's degree in Computer Science, Data Science, Information Technology, Statistics, or a closely related discipline
  • Certification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning – Specialty)
  • Certification in LLM engineering or generative AI (e.g., DeepLearning.AI Generative AI with LLMs)

Source: Brillio 2 careers (Lever)

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