Senior AI ML Engineer

Armakuni; Ahmedabad District; On-site; INR 3,200,000 - 5,200,000; Full time

Job summary

Armakuni is seeking a Senior AI/ML Engineer to design and scale generative AI and ML solutions. You will work across NLP, CV, and data science domains, building scalable pipelines and deploying models on cloud platforms.

The role emphasizes hands-on experimentation, model tracking, and production-grade systems using tools like SageMaker, LangChain, and PyTorch. On-site in India, with opportunities to advance in AI research.

Qualifications

• 2-3 years of experience in implementing, deploying, and scaling Generative AI solutions.
• 3-7 years of experience in NLP, Data Science, Machine Learning, and Computer Vision.
• Proficiency in Python and ML frameworks such as LangChain, Langfuse, LLAMA Index, LangGraph, CrewAI, and LLM output evaluations.
• Experience with AWS Bedrock, OpenAI GPT models (GPT-4, GPT-4o, GPT-4o-mini), and LLMs such as Claude, LLaMA, Gemini, and DeepSeek.

Responsibilities

• Architect scalable Generative AI and ML applications using cloud platforms and modern technologies.
• Develop and fine-tune LLMs, build RAG pipelines, and evaluate AI outputs.
• Deploy, monitor, and retrain models using SageMaker and other tools.
• Work on CV tasks like image classification, object detection, and segmentation.

Skills

Generative AI

NLP

Machine Learning

Computer Vision

Python

LangChain

Langfuse

LLama Index

LangGraph

CrewAI

LLM Eval

AWS Bedrock

GPT-4

Vector databases

Pinecone

OpenSearch

FAISS

Chroma

SageMaker

Scikit-learn

XGBoost

LightGBM

CatBoost

PyTorch

TensorFlow

Docker

FastAPI

REST APIs

SQL

PostgreSQL

NoSQL

Redis

Memcached

Tools

LangChain

LLAMA Index

LangGraph

CrewAI

SageMaker

Docker

FastAPI

Job description

We are seeking Senior AI/ML Engineers with 3 to 6 years of experience in implementing, deploying, and scaling AI/ML solutions. This role involves working with generative AI, machine learning, deep learning, and data science to solve business challenges by designing, building, and maintaining scalable and efficient AI/ML applications.

Key Responsibilities:

AI:

• Architect scalable Generative AI and Machine Learning applications using AWS Cloud OR GCP cloud and other cutting-edge technologies.
• Extensive experience with LLMs and various prompt engineering techniques.
• Fine-tune and build custom LLMs.
• Deep understanding of LLM architecture and internal mechanisms.
• Experience with Langchain, Langgraph, Langfuse, Crew AI, LLM output evaluations, and agentic workflows.
• Build RAG (Retrieval-Augmented Generation) pipelines and integrate them with traditional applications.

Data Science & Machine Learning:

• Solve complex data science problems and uncover insights using advanced EDA techniques.
• Implement automated pipelines for data cleaning, preprocessing, and model re-training.
• Hands‑on experience with model experiment tracking and validation techniques.
• Deploy, track, and monitor models using AWS SageMaker.
• Strong knowledge of fundamental machine learning concepts, including supervised and unsupervised learning, deep learning, CNNs, and RNNs.
• Proficiency in working with databases for efficient data storage and retrieval.
• Experience with data warehouses and data lakes.

Computer Vision:

• Work on complex computer vision problems, including image classification, object detection, segmentation, and image captioning.

Skills & Qualifications:

• 2-3 years of experience in implementing, deploying, and scaling Generative AI solutions.
• 3-7 years of experience in NLP, Data Science, Machine Learning, and Computer Vision.
• Proficiency in Python and ML frameworks such as LangChain, Langfuse, LLAMA Index, LangGraph, CrewAI, and LLM output evaluations.
• Experience with AWS Bedrock, OpenAI GPT models (GPT-4, GPT-4o, GPT-4o-mini), and LLMs such as Claude, LLaMA, Gemini, and DeepSeek.
• Experience with vector databases like Pinecone, OpenSearch, FAISS, and Chroma, with a strong understanding of indexing mechanisms.
• Expertise in forecasting, time series analysis, and predictive analytics.
• Experience with classification, regression, clustering, and other ML models.
• Proficiency in SageMaker for model training, evaluation, and deployment.
• Hands‑on experience with ML libraries such as Scikit‑learn, XGBoost, LightGBM, and CatBoost.
• Experience with deep learning frameworks such as PyTorch and TensorFlow.
• Familiarity with Docker, Uvicorn, FastAPI, and Flask for REST APIs.
• Proficiency in SQL and NoSQL databases, including PostgreSQL and AWS DynamoDB.
• Experience with caching technologies such as Redis and Memcached.