Senior AI/ML Engineer - R01571454

Brillio 2
Gurgaon +1 more
On-site

Who this role is best for

Strong fit for candidates with hands-on experience in LLM-based applications and RAG systems who can design end-to-end AI agent workflows and optimize for production deployment.

Best fit for

  • Candidates with hands-on experience in building LLM-based applications and RAG pipelines
    — “at least 2 years specifically building LLM-based applications, RAG systems, or AI agent workflows”
  • Individuals skilled in deploying AI/ML solutions in production with a focus on reliability and edge cases
    — “Deploy and maintain AI/ML solutions in production environments, focusing on reliability, monitoring, and edge case handling”
  • Professionals who can systematically iterate on prompt engineering and validation layers
    — “Build and systematically iterate on prompt engineering layers, testing and refining prompts and chain-of-thought strategies”

Things to consider

  • The role demands a high level of proficiency in Python and specific AI/ML frameworks
    — “Advanced proficiency in Python”
  • Candidates must be able to work closely with data scientists on evaluation frameworks
    — “collaborating with data scientists to develop evaluation frameworks”

How to stand out

  • Highlight experience in production deployment and monitoring of AI/ML solutions
    — “Deploy and maintain AI/ML solutions in production environments”
  • Demonstrate systematic approaches to prompt engineering and validation in your resume
    — “Build and systematically iterate on prompt engineering layers”
  • Showcase your ability to optimize LLM costs using model routing and caching
    — “Expertise in LLM cost optimization strategies such as model routing, caching, and prompt compression”
  • Emphasize your background in RAG architectures and embedding models
    — “Deep understanding of RAG architectures, including embedding models”
  • Demonstrate your ability to implement feedback loops and improve agent performance
    — “Design and implement feedback loops that capture expert review data”
Pace · Fast PacedCollaboration · MediumAutonomy · HighDecision Impact · Individual

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

What success looks like

  • design and build end-to-end AI agent workflows
  • deploy and maintain AI/ML solutions
Typical background
2–4 years of experience building LLM-based applications

Skills & requirements

Required

LLM FrameworksPrompt EngineeringRAG PipelinesAI Agent Workflows

Preferred

Multi-agent OrchestrationContent GenerationLLM Cost Optimization

Stack & domain

AIMLLLMLangchainLlamaindexPrompt EngineeringRAGData Pipeline ToolsProduction DeploymentMonitoringMulti-agent OrchestrationContent GenerationTranslationDocument ProcessingEvaluation FrameworksLLM Cost OptimizationProblem SolvingTeamworkCommunicationTensorflow Developer CertificateAWS Certified Machine Learning SpecialtyOpenai Certified EngineerHugging Face Certified AI PractitionerTechnology

About the role

Original posting from Brillio 2 via Lever

Senior AI/ML Engineer

Job requirements:

Experience Range:

  • 2–4 years of experience, including at least 2 years specifically building LLM-based applications, RAG systems, or AI agent workflowsKey Responsibilities:
  • Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and robust solutions
  • Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embedding models, retrieval ranking, and context window management to maximize information accuracy and retrieval efficiency
  • Build and systematically iterate on prompt engineering layers, testing and refining prompts and chain-of-thought strategies to achieve consistent, high-quality outputs across diverse inputs
  • Implement tool orchestration within agent workflows by integrating agents with databases, rule engines, validation systems, and formatting tools to automate complex tasks
  • Establish automated quality checks and validation layers to proactively catch issues and ensure high output reliability before human review
  • Instrument solutions for measurement, collaborating with data scientists to develop evaluation frameworks and track solution performance against defined targets
  • Deploy and maintain AI/ML solutions in production environments, focusing on reliability, monitoring, and edge case handling
  • Design and implement feedback loops that capture expert review data and translate it into measurable improvements in agent performanceRequired Skills:
  • Advanced proficiency in Python
  • Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar
  • Expertise in prompt engineering and systematic prompt testing
  • Deep understanding of RAG architectures, including embedding models, vector stores, retrieval strategies, and re-ranking
  • Experience building multi-step agent workflows with tool use, branching logic, and robust error handling
  • Experience with production deployment and monitoring of AI/ML solutions
  • Experience with data pipeline tools and frameworks (KubeFlow, BentoML, Great Expectations, Evidently AI)Preferred Skills:
  • Experience with multi-agent orchestration frameworks
  • Background in content generation, translation, or document processing solutions
  • Familiarity with fine-tuning LLMs or training reward models
  • Experience implementing feedback loops or RLHF mechanisms
  • Expertise in LLM cost optimization strategies such as model routing, caching, and prompt compression
  • Experience with multi-modal AI systems including voice-to-text, document understanding, and image analysis
  • Experience with evaluation frameworks for generative AI, including automated scoring and human evaluation protocolsDesired Qualifications:
  • Bachelor's degree in Computer Science, Data Science, Information Technology, 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 or generative AI technologies (e.g., OpenAI Certified Engineer, Hugging Face Certified AI Practitioner)

Source: Brillio 2 careers (Lever)

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