GenAI Engineer

Key Responsibilities

• Research, design, fine-tune, and deploy

  LLMs and SLMs

  for production use cases.
• Build and implement

  RAG pipelines

  with vector databases for domain-specific knowledge integration.
• Develop and optimize

  embeddings

  for search, semantic retrieval, and personalization.
• Integrate LLMs into real-world applications via

  APIs, chatbots, or microservices

  .
• Experiment with

  custom model training, fine-tuning, and parameter-efficient techniques (LoRA, PEFT, QLoRA, adapters)

  .
• Ensure solutions are

  scalable, cost-optimized, and production-ready

  .
• Work closely with data, product, and engineering teams to align AI outputs with business goals.
• Stay ahead of the curve with the latest GenAI, NLP, and ML research advancements.

Required Skills & Experience

• 3 4 years of professional experience in

  AI/ML, NLP, or GenAI engineering

  .
• Strong background in

  LLMs

  (OpenAI GPT, LLaMA, Falcon, Mistral, Claude, etc.).
• Experience in

  RAG workflows

  using vector DBs like:

• Pinecone, Weaviate, Milvus, FAISS, Chroma

• Solid understanding of

  embeddings models

  (OpenAI, Hugging Face, Sentence-BERT, InstructorXL).
• Hands-on with

  SLMs

  (Phi-3, TinyLLaMA, DistilBERT, etc.) and techniques for efficiency.
• Proficiency in

  Python

  and frameworks such as:

• PyTorch, TensorFlow, Hugging Face Transformers, LangChain, LlamaIndex

• Knowledge of

  API frameworks

  : FastAPI, Flask, gRPC.
• Experience with

  cloud AI platforms

  :
• AWS (SageMaker, Bedrock), Azure AI, GCP Vertex AI.
• Familiarity with

  MLOps

  :
• MLflow, Weights & Biases, Kubeflow, Docker, Kubernetes, CI/CD pipelines.
• Experience in

  data preprocessing and pipelines

  : Pandas, NumPy, Spark (nice to have).

Nice to Have

• Exposure to

  multi-modal LLMs

  (text, vision, speech).
• Experience building

  chatbots, copilots, or enterprise GenAI apps

  .
• Knowledge of

  prompt engineering & evaluation frameworks

  (LangSmith, Promptfoo, Ragas).
• Hands-on with

  LLM optimization

  (quantization, pruning, distillation).
• Knowledge of

  security and compliance

  in AI systems.