Machine Learning Engineer- AWS

Taggd; Mumbai; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Taggd is seeking a Senior Machine Learning Engineer in Mumbai to design and develop advanced ML models, optimize them for scale, and deploy on AWS infrastructure. You will build robust RAG pipelines, work with state-of-the-art LLMs, and ensure reliable, scalable performance.

Applicants should have 3–7 years of experience in ML engineering, strong Python, and hands-on experience with AWS Bedrock, OpenSearch, and CI/CD pipelines. This role is based in Mumbai with on-site work expectations.

Qualifications

• Hands-on experience with RAG pipelines, prompt engineering and LLM-based applications.
• Agentic AI frameworks such as LangChain, LangGraph, CrewAI and LlamaIndex experience.
• Proficiency in Python (Pandas, NumPy) and experience with Git, REST APIs and CI/CD.

Responsibilities

• Design and develop advanced ML models for scalable deployment on AWS.
• Optimize and deploy models on AWS infrastructure ensuring reliability.
• Collaborate across teams to integrate AI capabilities into products.

Skills

GenAI & LLMs

Python

REST APIs

CI/CD

Git

Pandas

NumPy

FastAPI

Tools

LangChain

LangGraph

CrewAI

LlamaIndex

AWS Bedrock

SageMaker

OpenSearch

Job description

Interview Process - Screening >> AI Interview >> L1 Discussion

Role Overview

• Job Role: Senior Machine Learning Engineer (3–7 years of experience).
• Responsibilities: Designing and developing advanced ML models, and optimizing and deploying them on AWS infrastructure to ensure scalability and reliability.

Required Technical Skills

• GenAI & LLMs: Hands-on experience with RAG pipelines, Prompt Engineering, LLM-based applications, and Agentic AI frameworks (e.g., LangChain, LangGraph, CrewAI, LlamaIndex).
• AWS Bedrock & Services: Hands-on experience with AWS Bedrock (including Claude Haiku/Sonnet), AgentCore, Guardrails, and core services like Lambda, S3, IAM, and SageMaker.
• ML Architecture: Experience with Titan Embeddings, Amazon OpenSearch, vector-based retrieval, and document processing strategies like parsing, chunking, and re-ranking.
• Programming & Engineering: Proficiency in Python (Pandas, NumPy, FastAPI) and experience with Git, REST APIs, and CI/CD pipelines.

Preferred Qualifications

• Relevant AWS certifications (e.g., AI Practitioner, ML Engineer, or Solutions Architect).
• Experience with MLOps, Docker, Kubernetes, and model monitoring.
• Knowledge of OCR/Document Intelligence, NLP techniques, and databases like Redshift, SQL, or DynamoDB.
• Familiarity with other foundation models (e.g., OpenAI, Gemini, Anthropic) and working in Agile/DevOps environments.