Ai Ml Engineer

Innodata India; Dadri; Hybrid; INR 1,800,000 - 3,200,000; Full time

Job summary

Innodata India is seeking an experienced AI/ML engineer to design, develop, and deploy machine learning models for real-world applications. The role includes data preprocessing, feature engineering, model training, evaluation, and optimizing end-to-end ML pipelines for production use.

You will work with generative AI, LLMs, and RAG architectures, building AI-driven data products and APIs. Strong emphasis on data privacy, governance, and scalable cloud deployments is expected.

Qualifications

• Bachelor's or Master's degree in CS/AI/Data Science or related field.
• 6+ years of experience in AI/ML development.
• Experience with GenAI, NLP, LLM, or Conversational AI projects.
• Experience handling large-scale datasets.
• Exposure to data privacy frameworks and anonymization methodologies.
• Understanding of data governance, compliance, and ethical AI practices.

Responsibilities

• Design, develop, and deploy machine learning models for real-world business applications.
• Perform data preprocessing, feature engineering, model training, validation, and optimization.
• Evaluate model performance using appropriate metrics and improve model accuracy.
• Build end-to-end ML pipelines for production deployment.
• Develop applications using OpenAI, Azure OpenAI, Claude, Gemini, Llama, or similar foundation models.
• Design and optimize prompts for business use cases.
• Build Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
• Develop AI agents using frameworks such as LangGraph, CrewAI, AutoGen, or similar technologies.
• Fine-tune and customize models where applicable.

Skills

Python

SQL

JavaScript/TypeScript

Data Analysis

Education

Bachelor's or Master's in CS/AI/DS

Tools

Pandas

NumPy

Jupyter Notebook

LangChain

LlamaIndex

LangGraph

CrewAI

Hugging Face

Scikit-learn

TensorFlow

PyTorch

Job description

Key Responsibilities

AI & Machine Learning Development

• Design, develop, and deploy machine learning models for real-world business applications.
• Perform data preprocessing, feature engineering, model training, validation, and optimization.
• Evaluate model performance using appropriate metrics and improve model accuracy.
• Build end-to-end ML pipelines for production deployment.

Generative AI & LLM Applications

• Develop applications using OpenAI, Azure OpenAI, Claude, Gemini, Llama, or similar foundation models.
• Design and optimize prompts for business use cases.
• Build Retrieval-Augmented Generation (RAG) solutions using vector databases and enterprise knowledge repositories.
• Develop AI agents using frameworks such as LangGraph, CrewAI, AutoGen, or similar technologies.
• Fine-tune and customize models where applicable.

Data Engineering & Data Intelligence

• Process and analyze structured, semi-structured, and unstructured datasets.
• Generate embeddings and implement semantic search capabilities.
• Design scalable data pipelines supporting AI-driven products.
• Work with large datasets obtained through surveys, field operations, digital platforms, research programs, or enterprise systems.

Data Privacy & Commercial Data Products

• Develop processes to anonymize, pseudonymize, and de-identify datasets before downstream usage.
• Ensure removal of Personally Identifiable Information (PII) and sensitive attributes before data monetization initiatives.
• Support creation of privacy-compliant datasets for:
  • AI model training
  • Market intelligence
  • Research projects
  • Data products sold to clients and enterprises
• Collaborate with data governance and compliance teams to ensure adherence to privacy regulations.

API Development & Integration

• Build scalable APIs using FastAPI or Flask.
• Integrate AI models with web applications, mobile applications, and enterprise platforms.
• Develop microservices architecture supporting AI workloads.
• Implement monitoring, logging, and performance optimization for AI APIs.

Cloud & Deployment

• Deploy applications on AWS, Azure, or Google Cloud Platform.
• Containerize solutions using Docker.
• Support CI/CD pipelines for AI applications.
• Optimize inference pipelines for latency, scalability, and cost efficiency.

Required Technical Skills

Programming

• Python (Mandatory)
• SQL
• Basic JavaScript or TypeScript (Preferred)

AI / Machine Learning

• Supervised and Unsupervised Learning
• Model Training & Inference
• Feature Engineering
• Model Evaluation Techniques
• Classification, Regression, Clustering

Generative AI

• OpenAI
• Azure OpenAI
• Claude
• Gemini
• Prompt Engineering
• RAG Architecture
• LLM Evaluation Frameworks

Frameworks & Libraries

• LangChain
• LlamaIndex
• LangGraph
• CrewAI
• Hugging Face
• Scikit-learn
• TensorFlow or PyTorch

Data Handling

• Pandas
• NumPy
• Data Cleaning
• Data Transformation
• Embeddings
• Semantic Search

Databases

• SQL Databases (PostgreSQL, MySQL)
• Vector Databases:
  • Pinecone
  • Weaviate
  • ChromaDB
  • FAISS

Cloud & DevOps

• AWS / Azure / GCP
• Docker
• CI/CD Concepts
• Kubernetes (Preferred)

Tools

• Git
• Postman
• Jupyter Notebook
• REST APIs

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• 6+ years of experience in AI/ML development.
• Experience working on GenAI, NLP, LLM, or Conversational AI projects.
• Experience handling large-scale datasets.
• Exposure to data privacy frameworks and anonymization methodologies.
• Understanding of data governance, compliance, and ethical AI practices.