AI ML Engineer

Weekday AI (YC W21); Delhi; On-site; INR 800,000 - 2,000,000; Full time

Job summary

A leading tech firm is seeking a Mid-Senior level Machine Learning Engineer to design scalable ML pipelines and APIs. Candidates should have experience with MLflow, SageMaker, and GPU architectures, along with proficiency in Kubernetes and Docker. The role offers a salary range of Rs 800000 - Rs 2000000 and is located in Delhi. Ideal for candidates with strong programming skills and experience in model fine-tuning and optimization.

Qualifications

• Deep understanding of GPU architectures for ML workloads.
• Expertise in distributed training and serving of large language models (LLMs).
• Hands-on experience with MLflow, SageMaker, Vertex AI, and Azure AI.
• Proficient in DevOps and LLMOps practices, including Kubernetes and Docker.

Responsibilities

• Design and implement scalable ML pipelines for experiment tracking, model management, and retraining.
• Develop high-performance APIs for model inferencing.
• Reduce training time and resource requirements for large-scale model deployments.

Tools

MLflow

SageMaker

Vertex AI

Azure AI

Kubernetes

Docker

DeepSpeed

MLflow

Python

SQL

JavaScript

Job description

This range is provided by Weekday AI (YC W21). Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

This role is for one of the Weekday's clients
Salary range: Rs 800000 - Rs 2000000 (ie INR 8-20 LPA)
Min Experience: 6 years
Location: Bengaluru, Karnataka, NCR, Delhi
JobType: full-time

Requirements

Key Responsibilities

ML Pipeline Design

• Design and implement scalable ML pipelines for experiment tracking, model management, feature management, and model retraining.
• Develop APIs for high-performance model inferencing.
• Hands‑on experience with MLflow, SageMaker, Vertex AI, and Azure AI.

LLM Serving & GPU Architecture

• Deep understanding of GPU architectures for ML workloads.
• Expertise in distributed training and serving of large language models (LLMs).
• Skilled in model/data parallel training using frameworks such as DeepSpeed and serving frameworks like vLLM.

Model Fine‑Tuning & Optimization

• Fine‑tune and optimize LLM and LVM models for improved latency and accuracy.
• Reduce training time and resource requirements for large‑scale model deployments.

DevOps & LLMOps

• Proficient in DevOps and LLMOps practices, including Kubernetes, Docker, and container orchestration.
• Experience with LLM orchestration frameworks such as Flowise, Langflow, and Langgraph.

Technical Skills & Tools

• LLM Frameworks: Hugging Face OSS LLMs, GPT, Gemini, Claude, Mixtral, Llama
• LLMOps Tools: MLflow, Langchain, Langgraph, LangFlow, Flowise, LlamaIndex, SageMaker, AWS Bedrock, Vertex AI, Azure AI
• Databases/Data Warehouses: DynamoDB, Cosmos, MongoDB, RDS, MySQL, PostgreSQL, Aurora, Spanner, Google BigQuery
• Cloud Platforms: AWS, Azure, GCP
• DevOps Tools: Kubernetes, Docker, FluentD, Kibana, Grafana, Prometheus
• Programming & Scripting: Python, SQL, JavaScript

Bonus

AWS Professional Solutions Architect, AWS Machine Learning Specialty, Azure Solutions Architect Expert

Seniority level

Mid-Senior level

Employment type

Full-time

Job function

Other

Industries

IT Services and IT Consulting