Ai Ml Engineer-PAN India

Job Position Title:

Location

Years of experience

Responsibilities:

-Design, develop, and deploy AI/ML models for real-world business use cases across structured and unstructured data domains.
-Implement and fine-tune Large Language Models (LLMs) such as GPT, Llama, Mistral, Claude, Gemini, or Falcon for enterprise and domain-specific applications. -Build and integrate Generative AI solutions for text, image, or multimodal content generation using frameworks such as LangChain, LlamaIndex, or OpenAI API. -Develop AI agents and agentic workflows capable of reasoning, planning, and executing multi-step tasks using tool use, retrieval-augmented generation (RAG), and memory mechanisms. -Collaborate with product, engineering, and data teams to integrate AI capabilities into enterprise applications and customer solutions. -Design prompt engineering, context optimization, and feedback loops for improving model accuracy and reliability. -Build and maintain data pipelines, vector databases (e.g., Pinecone, FAISS, ChromaDB), and retrieval systems for LLM applications. -Evaluate model performance using relevant metrics and A/B testing frameworks; ensure continuous improvement through experimentation. -Implement and maintain responsible AI practices bias detection, model explainability, data governance, and privacy compliance. -Stay current with the latest research in Generative AI, Agentic AI, and Machine Learning frameworks to apply innovations in production use cases.

Mandatory skill sets:

- 4–8 years of experience in Machine Learning / AI Engineering, with at least 1–2 years in Generative AI or LLM-based projects.
-Strong programming skills in Python with proficiency in frameworks like PyTorch, TensorFlow, or Hugging Face Transformers. -Experience with LLM integration frameworks: LangChain, LlamaIndex, or OpenDevin-like agentic orchestration tools. -Solid understanding of NLP techniques, transformer architectures, embedding models, and vector databases. -Hands-on experience deploying AI solutions using cloud platforms such as AWS (SageMaker, Bedrock), Azure AI Studio, or GCP Vertex AI. -Experience with RAG pipelines, prompt engineering, and fine-tuning / parameter-efficient tuning (LoRA, PEFT, QLoRA). -Familiarity with containerization (Docker), API development (FastAPI/Flask), and MLOps tools for deployment and monitoring. -Understanding of data preprocessing, feature engineering, and model evaluation metrics for both classical ML and generative AI.

Preferred skill sets:

-Experience with multimodal models (text-to-image, text-to-audio, etc.) using tools such as Stable Diffusion, CLIP, or Whisper.
-Knowledge of knowledge graphs, semantic search, and context chaining for advanced AI reasoning. -Exposure to AutoGPT, CrewAI, or similar agentic frameworks. -Background in MLOps pipelines using Kubeflow, MLflow, or Weights & Biases. -Contributions to open-source AI projects or publications in applied AI. -Certifications in AI/ML or Cloud AI services (AWS, Azure, GCP).