Generative AI Engineer

Who

• Develop and optimize transformer-based NLP models, including LLM fine-tuning, evaluation, and prompt engineering.
• Build and deploy RAG pipelines by integrating LLMs with vector databases (FAISS, Pinecone, Chroma, Azure AI Search).
• Implement multi-agent orchestration frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) for advanced workflows.
  
• Apply LLM evaluation and guardrail frameworks (Guardrails AI, TruLens, RAIL) to ensure reliability and safety.
• Design and maintain end-to-end ML pipelines: ingestion, preprocessing, training, deployment, and monitoring.
• Apply MLOps/LLMOps best practices for automation, observability, and reliability in Azure ML or equivalent platforms.
• Work with knowledge graphs (Neo4j, RDF/OWL) and integrate them into GenAI workflows.
• Build scalable pipelines and workflows using Databricks integrated with cloud platforms.
• Write clean, production-grade code in Python, including microservices, REST APIs, and FastAPI.
• Collaborate cross-functionally with data, cloud, and product teams to solve business problems with AI.

What

• 3-6 years of experience in AI/ML engineering.
• Proficiency in deep learning frameworks (PyTorch, TensorFlow).
• Solid experience with transformer models, LLMs, and RAG architectures.
• Familiarity with multi-agent orchestration and observability tools (LangSmith, Arize, W&B).
• Strong coding expertise in Python, APIs, and microservices.
• Hands-on experience with Databricks and enterprise ML workflows.
• Familiarity with knowledge graphs and their applications in AI.
• Experience with CI/CD pipelines and cloud-native deployments.
• Strong analytical skills and ability to link AI to business outcomes.

Why us: