Ai Ml Engineer

Job Summary

AI/LLM Engineer

Key Responsibilities

1. LLM/SLM/VLM Development (Primary 50%)

• Fine-tune and train LLMs, SLMs, and VLMs using frameworks such as

  Hugging Face, PyTorch, TensorFlow

  .
• Build custom training pipelines for domain-specific datasets.
  
• Optimize model performance, accuracy, and latency.
  

2. Data Engineering & MLOps

• Work on data ingestion, processing, and preparation pipelines.
  
• Implement scalable MLOps workflows for deployment, monitoring, and retraining.
  
• Manage and optimize vector databases for embeddings and retrieval.
  

3. LLM Architecture & Embeddings

• Build solutions using

  transformers, embeddings, and vector stores

  .
• Develop retrieval-augmented generation (RAG) pipelines.
  
• Implement scalable serving architectures for LLM-based applications.
  

4. Logical Problem Solving & Real-time Projects

• Apply strong analytical thinking to solve real-world business problems.
  
• Develop end-to-end AI solutions from problem definition to deployment.
  

5. API Development

• Build and deploy microservices using

  Python FastAPI

  .
• Integrate model inference pipelines with backend services.
  

6. Prompt Engineering & Agent Design (25%)

• Design and optimize prompts, prompt chains, and agent workflows.
  
• Work with

  LangChain

  or similar frameworks to develop multi-step autonomous agents.
• Use ML libraries like

  Scikit-learn, Pandas, PyTorch/TensorFlow

  for supporting ML tasks.

7. Cloud AI/ML (AWS Preferred)

• Work with cloud services such as

  AWS SageMaker, Bedrock, Lambda

  (limited experience acceptable).
• Deploy and manage ML models in cloud production environments.
  

8. Evaluation & Guardrails

• Implement LLM evaluation strategies (BLEU, ROUGE, grounding tests, toxicity checks, etc.).
  
• Develop guardrails for safety, reliability, and prompt protection.
  

Required Skills

• Strong hands-on experience with LLM/SLM/VLM training and fine-tuning.
  
• Deep knowledge of embeddings, transformers, vector stores (FAISS, Pinecone, Milvus, etc.).
  
• Proficiency in

  Python

  , FastAPI, PyTorch, TensorFlow, Scikit-learn, Pandas.
• Experience with MLOps tools and pipelines.
  
• Logical thinking and structured problem-solving approach.
  
• Familiarity with AWS AI/ML services (SageMaker, Bedrock preferred).
  
• Experience with prompt engineering, agents, and LangChain.
  
• Understanding of LLM evaluation and safety guardrails.
  

Preferred Qualifications

• Experience building RAG, chatbots, agent frameworks, or GenAI apps.
  
• Experience working with multimodal LLMs or VLMs.
  
• Hands-on projects with real-world datasets and problem statements.
  
• Contributions to open-source AI/ML projects.