Experience : 4 to 12 years

Location : Pune & Kolkata(specific)

Key Skill Requirements :

Gen AI/Agentic AI -Python, RAG, Prompt Engg.

4- 16 yrs

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
Strong 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.

AI/ML, Python

4- 16 yrs

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.
AWS Bedrock fundamentals

AWS Neo, prompt engineering
Solid Python fundamentals
REST APIs and integration patterns
Basic understanding of multi agent workflows
2.5 years in Python / AI / backend development
Exposure to cloud-native development preferred

MLOps Engineer

4- 16 yrs

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.
Implement governance, security, and access controls for ML workflows.
Optimize infrastructure usage and ensure scalable model serving.
Support incident troubleshooting and continuous improvement of ML systems.
Strong Python and SQL skills.
Experience with ML platforms such as Databricks, MLflow, SageMaker.
Knowledge of CI/CD tools and DevOps practices.
Experience with cloud platforms (AWS, Azure, or GCP).
Understanding of model lifecycle management and monitoring concepts.
Familiarity with containerization and orchestration (Docker, Kubernetes) is a plus.
Experience working with large-scale data platforms and distributed processing.
Exposure to feature stores, model governance, and automated retraining workflows.

MS Copilot, power platform and copilot studio

4- 18 yrs

Primary Skills - Microsoft Power Platform Canvas Model Driven Dataverse CoPilot Studio
Total IT Experience 5 to 12 years Minimum 4 Years Experience Microsoft Power Platform Canvas Model Driven Dataverse CoPilot Studio
Minimum 1 year CoPilot experience
Deep understanding of Microsoft Azure Power Platform Microsoft 365 and Copilot extensibility
Experience in developing custom web resources for PowerApps
Skilled in creating and utilising custom connectors
Ability to consume APIs functions to retrieve and update data from
Design and secure and high performing Copilot solutions using Microsoft Copilot Azure OpenAI and Microsoft 365 integrations

📌 Data Scientist (Uttar Pradesh)
🏢 LTM
📍 Uttar Pradesh