Blumetra - AI Operations Engineer (3-6 yrs)

Designation : AI Ops Engineer.

Work Location : Hyderabad (Hybrid).

Experience : 3+ years.

Job Summary :

We are looking for a highly skilled AI Operations Engineer who specializes in Vector Databases and Prompt Engineering to join our AI and ML engineering team.

The ideal candidate will be responsible for managing the deployment, optimization, and operational stability of AI/ML systems while ensuring seamless integration and scalability of LLM-based applications.

You will work closely with data scientists, machine learning engineers, and product teams to ensure AI applications are production-ready and highly responsive.

Key Responsibilities :

- Design, implement, and maintain infrastructure for LLM-based and AI-driven systems, with a strong focus on Vector Databases such as Pinecone, Weaviate, FAISS, or Chroma.

- Develop and optimize prompt engineering strategies to improve performance and accuracy of large language model (LLM) outputs in real-world applications.

- Collaborate with ML and Data Science teams to deploy and monitor AI models in production.

- Ensure the reliability, scalability, and availability of AI services using best practices in DevOps and MLOps.

- Build pipelines to ingest, process, and store unstructured data for semantic search and retrieval using vector embeddings.

- Perform model evaluation, fine-tuning, and prompt iteration to support high-quality generative responses.

- Identify and resolve production issues related to LLM inference, latency, vector retrieval, and API stability.

- Automate and document operational procedures and ensure compliance with data security and performance SLAs.

Required Skills & Qualifications :

- Bachelors or Masters degree in Computer Science, Engineering, AI/ML, or related discipline.

- 3+ years of experience in AI/ML operations, with production-level experience in managing AI systems.

- Hands-on experience with Vector Databases like Pinecone, FAISS, Weaviate, ChromaDB, or Vespa.

- Strong understanding of embedding models, semantic search, and retrieval-augmented generation (RAG) pipelines.

- Proven experience in Prompt Engineering using OpenAI, Anthropic, Hugging Face, or similar LLM platforms.

- Experience with Python, LangChain, LLMOps tools, and cloud platforms (AWS, GCP, or Azure).

- Familiarity with MLOps and deployment frameworks like MLflow, Kubeflow, or SageMaker.

- Strong analytical and debugging skills with an understanding of model performance metrics.

Nice to Have :

- Experience with deploying custom-trained LLMs and managing token costs.

- Knowledge of multi-modal AI systems (text, image, audio).

- Understanding of API rate limits, batching strategies, and LLM lifecycle management.

- Exposure to vector similarity metrics, ANN search optimization, and embedding model selection.