Artificial Intelligence Engineer

Title

Experience

Location

Employment Type

About the Role-

We are seeking an AI / Generative AI Engineer to join our ML/AI team. In this role, you will work on building, fine-tuning, and deploying NLP and multimodal models, developing GenAI-driven features, and ensuring their scalability, reliability, and security in production. The position requires a balance of hands-on model development, prompt and dataset engineering, and strong engineering practices.

Key Responsibilities

• Design, fine-tune, and evaluate transformer-based generative models for tasks such as summarization, Q&A, code generation, and RAG.
• Build and maintain data pipelines for model training and evaluation, including dataset collection, cleaning, labeling, and augmentation.
• Develop, test, and optimize prompt engineering strategies; track performance drift.
• Create and manage RAG pipelines (embeddings, vector stores, index creation, and retriever tuning).
• Containerize ML services using Docker and build deployable inference endpoints with FastAPI / Flask / .NET.
• Support deployments on Kubernetes, serverless frameworks, or cloud platforms.
• Implement monitoring, logging, and evaluation metrics to detect performance, data, and feature drift.
• Collaborate with product and infrastructure teams to integrate AI features into applications with best-in-class security (rate limits, PII redaction, moderation).
• Stay updated on emerging models and benchmark third-party APIs (OpenAI, Anthropic, Meta, etc.).
• Create clear documentation, runbooks, and reproducible experimentation workflows.

Required Qualifications

• 2 to 3 years of experience in applied ML, NLP, or generative AI.
• Strong Python skills with experience in PyTorch (preferred) or TensorFlow.
• Models: Logistic Regression, Decision Trees, Random Forests, Neural Networks, RNN, LSTM, EncoderDecoder, Attention, Transformers, BERT, GPT
• Generative AI: OpenAI, Mistral, LLaMA, Gemini, Claude, Hugging Face, RAG, Fine-tuning, AI Agents
• LLM Tools: LangChain, LangSmith, LangGraph, AutoGen, CrewAI, Azure AI Foundry
• Vector Databases: FAISS, ChromaDB, Pinecone, Qdrant, Weaviate
• Backend: FastAPI, Flask, REST APIs, Redis, RabbitMQ, Node.js, WebHooks, Postman, Swagger
• Understanding of model evaluation metrics (ROUGE, BLEU, Accuracy, F1, safety metrics).
• Experience with LLM orchestration frameworks (LangChain, LlamaIndex, LangGraph, AutoGen).

Preferred / Nice-to-Have-

• Familiarity with prompt engineering techniques and templates.
• Experience with cloud platforms (AWS, GCP, Azure) and managed ML services (SageMaker, Vertex AI, Bedrock).
• Understanding of model security and privacy practices (PII redaction, moderation).
• Experience with ML monitoring tools (Prometheus, Grafana).

Deliverables / KPIs (First 36 Months-

• Deliver at least one end-to-end GenAI feature (prototype staging) with evaluation results.
• Set up reproducible fine-tuning pipelines and experiment tracking.
• Deploy a production-ready inference service with monitoring and cost controls.
• Create prompt templates with a documented rollback strategy for model updates