Generative AI Engineer

Job Title: Generative AI Engineer

Location: Hyderabad (onsite)

Key Responsibilities:

High-Throughput RAG Pipeline Development:

• Design and build scalable document processing pipelines to ingest and semantically chunk large batches of documents (PDF/DOCX) from sources like Azure Blob and AWS S3.
  
• Integrate embedding models and tune vector databases like Milvus for high-performance, sub-100 ms k-NN retrieval.
  
• Implement hybrid retrieval systems using BM25 and vector search, and continually track and improve retrieval performance using metrics like MRR and recall@k.
  

Model Fine-Tuning & Prompt Engineering:

• Apply large language models (LLMs) and NLP techniques to solve complex problems such as named-entity recognition, question answering, and summarization.
  
• Build fine-tuning pipelines using frameworks like LoRA/PEFT and run hyperparameter sweeps in Azure ML.
  
• Author multi-step prompt chains, enforce structured JSON outputs, and use validation guards to reduce hallucinations and improve model consistency.
  

MLOps & Production Deployment:

• Develop and containerize agent-based microservices using frameworks like FastAPI or Azure Functions.
  
• Define Infrastructure as Code using Terraform/ARM and build CI/CD workflows in GitHub Actions for automated testing and canary rollouts.
  
• Implement robust monitoring and alerting for latency (p50/p95) and error rates using tools like Prometheus, Grafana, or Azure Monitor to ensure SLA compliance.
  

Performance, Cost & Standards:

• Profile API calls and implement cost-reduction strategies like batching, caching, and early-stop logits.
  
• Produce high-quality documentation, including architecture diagrams, sequence flows, and data schemas.
  
• Enforce security and compliance standards, including data encryption and PII redaction, to align with HIPAA/GxP requirements.
  

Required Qualifications:

• BS/MS in Computer Science, AI/ML, or a related field.
  
• 3+ years of experience

  building end-to-end LLM/RAG systems

  in a production environment.
• Deep

  Python experience, including libraries like FastAPI, pandas, and NumPy.

• Hands-on experience with

  LLM orchestration frameworks (LangChain, LlamaIndex), NLP libraries (Hugging Face), and OpenAI/Azure SDKs.

• Proven expertise in MLOps, including CI/CD (GitHub Actions/Azure DevOps) and containerization (Docker/Kubernetes).
  

Preferred Qualifications (Nice-to-Haves):

• Experience working in a regulated industry such as

  pharmaceuticals or life sciences.

• Hands-on experience with

  vector databases like Milvus or Pinecone.

• Familiarity with

  chatbot frameworks like Rasa or Botpress.

• Experience with data-centric AI tools for validation and monitoring, such as Great Expectations or Deepchecks.