AI Engineer

Jain International Trade Organisation - India; Gurugram District; On-site; INR 200,880 - 334,800; Full time

Job summary

Jain International Trade Organisation - India in Gurugram is looking for a management-level Data Scientist to oversee AI implementations and lead cross-functional teams. The ideal candidate has 5–8 years of experience in Machine Learning and strong programming skills in Python.

You will work closely with various departments to translate business needs into data-driven solutions while ensuring compliance in AI practices. This is a full-time, onsite position.

Qualifications

• Strong foundation in statistics, probability, and machine learning.
• 5–8 years of professional experience in Data Science or Machine Learning.
• Hands-on experience in building, training, and deploying ML models.

Responsibilities

• Implementing autonomous agents or workflows using agentic AI concepts.
• Working knowledge of key AWS services such as S3 and Lambda.
• Communicating findings and model behavior to non-technical stakeholders.

Skills

Machine Learning

MLOps

Python

AWS services

Generative AI

Education

Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics

Tools

Amazon SageMaker

TensorFlow

scikit-learn

Docker

Kubernetes

Job description

Minfy Technologies • Gurugram • Posted 2 days ago • Updated 2 days ago

Job Summary

Job Type: Full Time Permanent

Location: Gurugram

Work Mode: Onsite

Experience Level: Management

Required Qualifications

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Academic Foundation: Strong foundation in statistics, probability, and machine learning.
• Core Experience: 5–8 years of professional experience in Data Science, Machine Learning, or Applied AI.
• Hands‑on ML Expertise: Building, training, and deploying ML models in production environments; strong understanding of data preprocessing, feature engineering, and model evaluation.
• End‑to‑End ML Workflow Experience: Working on complete ML pipelines under guidance of senior/principal team members; translating business and product requirements into data‑driven solutions.

Technical Skills

• Machine Learning & MLOps:
  • Practical experience using Amazon SageMaker for model training, tuning, deployment, and inference.
  • Experience with common ML algorithms using scikit‑learn, TensorFlow, or PyTorch.
  • Knowledge of MLOps concepts: model versioning, experiment tracking, monitoring, retraining workflows.
  • Working knowledge of Docker and exposure to Kubernetes‑based deployments.
  • Collaboration with ML engineers to operationalize models.
• Generative AI & LLMs:
  • Hands‑on experience developing LLM‑powered applications using Amazon Bedrock or similar platforms.
  • Building or contributing to Retrieval‑Augmented Generation (RAG) pipelines: document ingestion and chunking, embedding generation, similarity search using vector databases.
  • Practical knowledge of prompt engineering, prompt tuning, output evaluation.
  • Understanding of LLM failure modes (hallucinations, grounding issues) and ability to evaluate responses for accuracy, relevance, and safety.

Responsibilities

• Agentic AI (Growing Expertise): Exposure to agentic AI concepts and frameworks such as AgentCore; implementing
  • Simple autonomous agents or workflows.
  • Multi‑step reasoning with predefined tools.
  • Tool‑calling, agent orchestration, workflow automation.
  • Implement guardrails, human‑in‑the‑loop mechanisms, logging and observability for agent behavior.
• Cloud Platform: Working knowledge of key AWS services, including S3, Lambda, Redshift, IAM.
• Professional & Collaboration Skills:
  • Strong Python programming skills and experience working with APIs.
  • Ability to work effectively in cross‑functional teams (product, engineering, analytics).
  • Willingness to learn quickly in a fast‑evolving AI landscape.
  • Comfortable taking technical guidance and implementing feedback.
  • Clear communication of findings, limitations, and model behavior to non‑technical stakeholders.
• Nice to Have:
  • Initial exposure to AI governance, compliance, or ethical AI practices.
  • Experience with monitoring LLM outputs and basic evaluation frameworks.
  • Certifications in AWS, Machine Learning, or Data Science.
  • Prior experience in enterprise or cloud‑native environments.