Ai Ml Engineer

Key Responsibilities

1. Model Training & Fine-Tuning

• Fine-tune and train

  LLMs, SLMs, and VLMs

  (Llama, Mistral, Falcon, CLIP, BLIP, Qwen-VL, etc.).
• Hands-on experience with

  Hugging Face Transformers

  ,

  PyTorch

  , or

  TensorFlow

  .
• Build and optimize model training pipelines (PEFT, LoRA, QLoRA, quantization, pruning).
  
• Prepare and manage datasets for training/fine-tuning.
  

2. LLM Engineering & Data Pipelines

• Build and manage

  LLM/Transformer pipelines

  , embeddings, tokenization, and vector search.
• Experience working with vector databases like

  FAISS, Pinecone, Weaviate, Chroma

  .
• Build

  RAG pipelines

  and implement embedding-based retrieval systems.
• Strong understanding of transformers and model architecture.
  

3. MLOps & Deployment

• Work with MLOps tools (MLflow, DVC, Airflow, Docker, CI/CD).
  
• Deploy ML and LLM workloads using

  FastAPI

  , microservices, and cloud-native solutions.

4. Prompt Engineering + Agent Systems

• Build

  prompt tuning, prompt chaining

  , and multi-step reasoning workflows.
• Experience with

  Agent-based frameworks

  (LangChain Agents, CrewAI, AutoGen, etc.).
• Strong understanding of generative AI orchestration patterns.
  

5. Cloud (AWS Preferred)

• Experience with AWS ML/AI services:
  

• SageMaker

• Bedrock

• Lambda / API Gateway

• Ability to deploy and scale AI models in cloud environments.
  

6. Evaluation & Guardrails

• Implement LLM evaluation methods (RAGAS, DeepEval, OpenAI Evals).
  
• Build and enforce

  LLM guardrails

  , safety filters, and moderation workflows.

7. General Skills

• Strong Python programming and FastAPI development skills.
  
• Excellent logical thinking and ability to solve real-world problems.
  
• Ability to work in fast-paced environments and deliver POCs/solutions quickly.
  

Required Skills

• 4+ years of experience in AI/ML/GenAI.
  
• Strong knowledge of LLM training, tuning, and deployment.
  
• Hands-on with HuggingFace, PyTorch, Transformers.
  
• Experience with embeddings and vector databases.
  
• FastAPI, Python, MLOps tools.
  
• AWS AI/ML experience (good to have).
  
• Agent frameworks and RAG implementations.
  
• Understanding of evaluation metrics and guardrails.