AI/Gen AI Engineer

We need an experienced skilled AI / Generative AI Developer to design, build, and deploy intelligent solutions using modern machine learning, deep learning, and large language models (LLMs). The role involves developing scalable AI systems, fine tuning foundation models, integrating GenAI into enterprise platforms, and collaborating with cross functional teams to deliver business impacting AI products.

What are my responsibilities

As an AI Developer, you are required to:

• Design and develop machine learning and deep learning models for structured and unstructured data
• Be a Full Stack GenAI Engineer, including UI development, LLM orchestration (using LLMs, APIs, external data sources).
• Build end to end ML pipelines covering data ingestion, preprocessing, training, evaluation, and deployment
• Optimize model performance, latency, scalability, and cost
  
  

Qualification: Bachelor's or Master's degree in Computer Science & Engineering. Additional courses(s) on AI, ML topics; knowledge of statistics is preferred.

Experience level: Minimum 4-7 years in software development with at least 3 years hands-on Development experience in AI / ML.

Knowledge & Experience:

Programming:

• Language: Python.
• JavaScript / TypeScript frontend & full stack GenAI apps
• Knowledge of REST APIs, microservices, and containerization (Docker, Kubernetes) (GraphQL will be an advantage).
• Knowledge / Working experience with SQL / NoSQL databases
  
  

Generative AI & LLMs

Develop applications using Large Language Models (LLMs) such as GPT, LLaMA, Claude, or similar. Fine tuning of models.

Understanding of - Context windows and token limits

2 Implement prompt engineering methods:

Zero shot, few shot prompting

Chain-of-Thought prompting

Prompt templates

Handling hallucinations

• RAG (Retrieval Augmented Generation)
  
  

Embeddings & vector similarity

Chunking strategies

Semantic search

Knowledge grounding

• Vector databases
  
  

Pinecone,

Milvus

Azure AI Search

• Data Handling
• Data cleaning & preprocessing of Structured + unstructured data
  
  

(Eg., PDFs, documents, logs, emails )

• Cloud, MLOps & Deployment
• Azure - Cloud
  
  

Model Deployment

• Docker, containers
• REST APIs (FastAPI, Flask)
• Serverless functions
  
  

Knowledge on MLOps / LLMOps - desirable

• Model versioning
• Monitoring drift & performance
• Model Validation: Evaluate hallucination, bias, safety, and reliability of GenAI outputs. Validation of conventional ML approaches. Metrics ( accuracy, precision, recall, ROUGE, BLEU, etc.)
• Experience in LLM tools / Frameworks
  
  

Hugging Face (Transformers, Datasets)

LangChain / LlamaIndex

OpenAI / Azure OpenAI SDKs

Sentence Transformers

Engineering Practices and concepts :

Object-Oriented & Functional Programming concepts

Unit testing & integration testing

Machine Learning & AI Foundations

Overview of Classical ML

Core Concepts

Supervised vs Unsupervised learning

Model training, validation, overfitting

Feature engineering

Required Soft skills & Other Capabilities:

• Team Orientation:
  
  

Actively contributes to a collaborative team environment and supports joint problem-solving.

• Independent Work Style:
  
  

Able to manage tasks independently, prioritize effectively, and deliver results with minimal supervision.

• Systematic Thinking:
  
  

Approaches problems with structured, analytical reasoning and helps deliver scalable, maintainable solutions.

• Willingness to Learn
  
  

Open to acquiring new knowledge and adapting to evolving technologies and processes.

• Communication skills: Adequate communication skills in order to explain your work to people who don't understand the mechanics behind data analysis
  
  

Proactive Communication:

Communicates clearly, raises issues early, and maintains transparency within the team.