AI Engineer

Minfy Technologies; Gurugram District; On-site; INR 1,200,000 - 2,000,000; Full time

Job summary

Minfy Technologies is seeking an experienced Data Scientist in Gurugram. The ideal candidate will have 5 to 8 years of experience in Data Science and Machine Learning, with strong skills in statistics, Python, and experience with Amazon SageMaker and various ML frameworks. Responsibilities include building, training, and deploying ML models, and developing LLM-powered applications. The role emphasizes collaboration, communication, and the ability to adapt within the evolving AI landscape.

Qualifications

• 5–8 years of professional experience in Data Science, Machine Learning, or Applied AI.
• Hands-on experience building, training, and deploying ML models in production environments.
• Strong understanding of data preprocessing, feature engineering, and model evaluation.

Responsibilities

• Translate business and product requirements into data-driven solutions.
• Collaborate with ML engineers to operationalize models.
• Develop LLM-powered applications using Amazon Bedrock or similar platforms.

Skills

Data Science

Machine Learning

Statistics

Python

Amazon SageMaker

Docker

TensorFlow

scikit-learn

PyTorch

Education

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

Tools

Amazon SageMaker

Docker

AWS services (S3, Lambda, Redshift, IAM)

Job description

Educational Requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field
• Strong academic 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 experience building, training, and deploying ML models in production environments
• Strong understanding of data preprocessing, feature engineering, and model evaluation
• Experience working on end‑to‑end ML workflows under guidance of senior/principal team members
• Ability to translate business and product requirements into data‑driven solutions
• Practical experience using Amazon SageMaker for:
  • Model training and tuning
  • Model deployment and inference
• Experience with common ML algorithms using scikit‑learn, TensorFlow, or PyTorch
• Familiarity with MLOps concepts such as:
  • Experiment tracking
  • Basic monitoring and retraining workflows
• Working knowledge of Docker and exposure to Kubernetes‑based deployments
• Experience collaborating with ML engineers to operationalize models

Generative AI & LLMs

• Hands‑on experience developing LLM‑powered applications using Amazon Bedrock or similar platforms
• Experience building or contributing to Retrieval‑Augmented Generation (RAG) pipelines, including:
  • Document ingestion and chunking
  • Similarity search using vector databases
• Practical knowledge of prompt engineering, prompt tuning, and output evaluation
• Understanding of common LLM failure modes such as hallucinations and grounding issues
• Ability to evaluate LLM responses for accuracy, relevance, and safety

Agentic AI (Growing Expertise)

• Exposure to Agentic AI concepts and frameworks such as AgentCore
• Experience implementing:
  • Simple Autonomous agents or workflows
  • Multi‑step reasoning with predefined tools
  • Familiarity with tool‑calling, agent orchestration, and workflow automation
• Understanding the importance of:
  • Guardrails
  • Human‑in‑the‑loop mechanisms
  • Logging and observability for agent behavior
• 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