Generative AI / LLM Engineer with Cloud

Generative AI / LLM Engineer

Key Responsibilities

• LLM Development & Fine-Tuning

  – Build, train, and optimize large language models for domain-specific use cases.

• Agentic AI Solutions

  – Design and implement autonomous or semi-autonomous AI agents capable of multi-step reasoning, decision-making, and tool orchestration.

• Generative AI Applications

  – Create innovative applications leveraging generative AI for text, data extraction, summarization, and reasoning tasks.

• Data Engineering & Processing

  – Use Python and SQL to build data pipelines, preprocess datasets, and optimize data storage for LLM workflows.

• Cloud-Native AI Deployment

  – Develop and deploy AI solutions using

  Google Cloud Platform (Vertex AI, BigQuery, AI Platform, Cloud Functions, Pub/Sub)

  , with experience in

  Azure

  or

  AWS

  as a plus.

• Integration & Scalability

  – Implement AI models into production environments, ensuring scalability, reliability, and cost efficiency.

• RAG & Vector Search

  – Build retrieval-augmented generation pipelines using vector databases (Pinecone, Weaviate, FAISS, Milvus, etc.).

• Research & Innovation

  – Stay ahead of emerging trends in LLM architectures, prompt engineering, RAG, and agent frameworks.

• Collaboration

  – Work closely with cross-functional teams (data engineering, product, cloud architects) to align AI capabilities with business goals.

Required Skills & Qualifications

• Proven experience in

  Generative AI

  and

  LLM development

  (OpenAI, Anthropic, LLaMA, Mistral, etc.).
• Hands-on experience with

  Agentic AI

  concepts, tools, and frameworks (e.g., LangChain, CrewAI, Semantic Kernel).
• Strong

  Python

  programming skills for AI model development and automation.
• Solid experience with

  SQL

  for data extraction, transformation, and analysis.
• Strong cloud expertise,

  especially Google Cloud Platform (Vertex AI, BigQuery, GCS, Cloud Run, Pub/Sub)

  .
• Experience with

  RAG architectures

  and vector databases.
• Familiarity with MLOps concepts for deploying and monitoring AI models in production.
• Strong problem-solving skills and ability to work in fast-paced, innovative environments.
  

Preferred Qualifications

• Multi-cloud experience (Azure OpenAI, AWS Bedrock, GCP Vertex AI).
• Knowledge of AI governance, compliance, and responsible AI practices.
• Experience in integrating AI into enterprise applications via APIs and microservices.