ML Engineer II

Pibit.ai; Bengaluru; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Pibit.ai is seeking an experienced ML Engineer (MLE-2) to design, implement, and optimize AI solutions across a full ML lifecycle. You will lead from development to deployment, ensuring model success and impact while collaborating with cross-functional teams.

You will work with state-of-the-art NLP and generative AI techniques, build scalable data pipelines, and implement LLM-based features, ensuring production-grade performance on cloud and Kubernetes environments.

Qualifications

• Experience building and deploying AI-powered features.
• Strong knowledge of ML lifecycle from data to deployment.
• Experience with experiment tracking and model monitoring.

Responsibilities

• Design and implement AI product features.
• Maintain and optimize AI systems.
• Train, evaluate, deploy, and monitor ML models.
• Design ML pipelines for experiment, model, and feature management.
• Implement A/B testing and scalable model inferencing APIs.
• Collaborate with cross-functional teams to build scalable ML solutions.
• Deploy and monitor ML models for performance and reliability.

Skills

Hugging Face LLMs

GPT

Gemini

Claude

Mixtral

Llama

Tools

MLFlow

Langchain

Langgraph

LangFlow

Langfuse

LlamaIndex

SageMaker

AWS Bedrock

Azure AI

MongoDB

PostgreSQL

Pinecone

ChromDB

AWS

Azure

Kubernetes

Docker

Python

SQL

JavaScript

AWS Professional Solution Architect

AWS Machine Learning Specialist

Azure Solutions Architect Expert

Job description

As an MLE-2, you will design, implement, and optimize AI solutions while ensuring model success. You will lead the ML lifecycle from development to deployment, collaborate with cross-functional teams, and enhance AI capabilities to drive innovation and impact.

Responsibilities:

• Design and implement AI product features.
• Maintain and optimize existing AI systems.
• Train, evaluate, deploy, and monitor ML models.
• Design ML pipelines for experiment, model, and feature management.
• Implement A/B testing and scalable model inferencing APIs.
• Optimize GPU architectures, parallel training, and fine-tune models for improved performance.
• Deploy LLM solutions tailored to specific use cases.
• Ensure DevOps and LLMOps best practices using Kubernetes, Docker, and orchestration frameworks.
• Collaborate with cross-functional teams to design and build scalable ML solutions.
• Implement state-of-the-art ML techniques, including NLP, generative AI, RAG, and transformer architectures.
• Deploy and monitor ML models for high performance and reliability.
• Innovate through research, staying ahead of industry trends.
• Build scalable data pipelines following best practices.

Requirements:

• LLM and ML: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, and Llama.
• LLMOps: MLFlow, Langchain, Langgraph, LangFlow, Langfuse, LlamaIndex, SageMaker, AWS Bedrock, Azure AI.
• Databases: MongoDB, PostgreSQL, Pinecone, ChromDB.
• Cloud: AWS, Azure.
• DevOps: Kubernetes, Docker.
• Languages: Python, SQL, JavaScript.
• Certifications (Bonus): AWS Professional Solution Architect, AWS Machine Learning Specialist, Azure Solutions Architect Expert.