Skill : Gen AI, AIML, Agentic AI, Python

Along with any cloud combination AWS/Azure or GCP

Job Description 1:Gen AI/Agentic AI -Python, RAG, Prompt Engg.

- Hands-on experience in Data Science, AI/ML, and Deep Learning (DL) domains
- Practical exposure to Generative AI (GenAI) and DL frameworks
- Robust programming skills in Python and experience with Linux/Windows environments
- Proficiency with common data science toolkits:TensorFlow, Keras, PyTorch, Pandas, NumPy, etc.
- Familiarity with GenAI frameworks: LangChain, LlamaIndex, LangGraph, CrewAI, NeMo Agent Toolkit, NAT
- Knowledge of DL & GenAI algorithms: Transformers, CNN, RNN, etc.
- Experience with NLP, Large Language Models (LLMs), and Multimodal Language Models like:GPT, LLaMa, Mistral, Qwen, Nemotron, etc.
- Hands-on experience with Cloud platforms:Azure, AWS, or GCP
- Familiarity with Git, Docker, and Kubernetes
- Strong problem-solving skills.

Job Description 2: AI/ML, Python

Role Overview

The AI Engineer focuses on developing and enhancing AI capabilities under the guidance of senior engineers and architects.

Key Responsibilities

- Develop AI features using AWS Bedrock.
- Implement Python-based services and utilities.
- Build prompt flows and agent tools.
- Integrate AI services with upstream/downstream systems.




- Write clean, testable, and reusable code.
- Support unit testing and bug fixes.
- Participate in code reviews and sprint deliveries.

Key Skill Set :

- AWS Bedrock fundamentals
- AWS Neo, prompt engineering
- Strong Python fundamentals
- REST APIs and integration patterns
- Basic understanding of multi agent workflows

Job Description 3: MLOps Engineer

Machine Learning Operations (ML Ops) Engineer

Role Overview

The ML Ops Engineer is responsible for operationalizing machine learning models by building reliable, scalable, and governed pipelines that support the full ML lifecycle from development to production. The role ensures models are deployed efficiently, monitored continuously, and integrated seamlessly into data and business platforms.

Key Responsibilities

- Build and maintain end to end ML pipelines including data preparation, model training, deployment, and monitoring.
- Automate model deployment using CI/CD practices.
- Manage model versioning, experiment tracking, and reproducibility.
- Monitor model performance, data drift, and system reliability in production.

Collaborate with data scientists and data engineers to productionize models

📌 Data Science Engineer (Pune)
🏢 LTM
📍 Pune