Senior AI/ML Engineer - R01571454

Lever, Inc.; Gurugram District; On-site; INR 2,400,000 - 4,000,000; Full time

Job summary

Lever, Inc. in Gurugram seeks a Senior AI/ML Engineer to design and deploy end-to-end AI agent workflows and RAG pipelines, with a focus on robust, scalable solutions.

The role requires 2–4 years in AI/ML, strong Python skills, and hands-on experience with LangChain/LlamaIndex and production deployment. You will optimize embeddings, contexts, and prompt strategies for high-quality outputs.

Qualifications

• 2–4 years of experience, including at least 2 years building LLM-based applications, RAG systems, or AI agent workflows.
• Experience designing and deploying end-to-end AI agent workflows from prompts to production.
• Strong Python proficiency and hands-on experience with LLM frameworks.

Responsibilities

• Design and build end-to-end AI agent workflows, from initial prompt design through production deployment.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines and context management.
• Iterate on prompt engineering layers to ensure high-quality outputs.
• Implement tool orchestration by integrating agents with databases, rule engines, and formatting tools.
• Establish automated quality checks and validation layers for reliability.
• Collaborate with data scientists to monitor performance against targets.
• Deploy and maintain AI/ML solutions in production with focus on reliability.
• Design feedback loops to translate expert reviews into improvements.

Skills

Python

LangChain

LlamaIndex

Prompt engineering

RAG architectures

Agent workflows

Production deployment

Education

Bachelor's degree in Computer Science or related

TensorFlow Developer Certificate

AWS Certified ML Specialty

OpenAI Certified Engineer

Hugging Face Certified AI Practitioner

Tools

KubeFlow

BentoML

Great Expectations

Evidently AI

Job description

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 workflows

Key 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 performance

Required 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 protocols

Desired 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)