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
3. RAG (Retrieval Augmented Generation)
Embeddings & vector similarity
Chunking strategies
Semantic search
Knowledge grounding
4. Vector databases
Pinecone,
Milvus
Azure AI Search
5. Data Handling
• Data cleaning & preprocessing of Structured + unstructured data
(Eg., PDFs, documents, logs, emails )
6. 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
7. Model Validation: Evaluate hallucination, bias, safety, and reliability of GenAI outputs. Validation of conventional ML approaches. Metrics ( accuracy, precision, recall, ROUGE, BLEU, etc.)
8. 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.
