Senior AI/ML Engineer

Key Responsibilities

• LLM Integration:

  Develop and integrate LLMs (OpenAI, Azure OpenAI, Anthropic, or open-source models) into enterprise applications and intelligent assistants.

• Embeddings & Vector Search:

  Build and optimize embedding pipelines and semantic search systems using vector databases such as

  FAISS

  ,

  Pinecone

  ,

  Weaviate

  ,

  Milvus

  , or

  Azure Cognitive Search

  .

• Recommendation Systems:

  Design and implement hybrid recommendation systems (content-based, collaborative, and LLM-driven) for personalization and discovery.

• Model Serving & Deployment:

  Deploy, serve, and monitor models in production using

  Azure ML

  ,

  Docker

  ,

  Kubernetes

  , or

  FastAPI

  , ensuring scalability and low latency.

• Data Preparation & MLOps:

  Collaborate with data engineers to prepare training data and automate model lifecycle management (CI/CD, monitoring, retraining).

• Optimization & Cost Efficiency:

  Fine-tune large models, optimize vector search pipelines, and manage compute costs effectively.

• Research & Innovation:

  Stay current with emerging trends in

  RAG (Retrieval-Augmented Generation)

  ,

  generative AI

  , and

  multimodal systems

  to propose innovative use cases and solutions.

Required Skills & Qualifications

• Bachelors or Master’s degree in Computer Science, Data Science, or a related field.

• 4+ years

  of professional experience in

  Machine Learning

  and

  AI product development

  .
• Strong expertise in

  Python

  and ML libraries: PyTorch, TensorFlow, Hugging Face Transformers, and Scikit-learn.
• Proven experience with

  LLM-based systems

  ,

  embeddings

  , and

  vector database integrations

  .
• Hands-on experience with

  Azure ML

  ,

  Azure Cognitive Search

  , and

  OpenAI API

  (or similar platforms).
• Deep understanding of

  recommendation algorithms

  and personalization systems.
• Experience deploying models using

  Docker/Kubernetes

  ,

  FastAPI

  , or similar frameworks.
• Solid understanding of

  MLOps pipelines

  ,

  CI/CD

  , and

  cloud automation

  best practices.

Good-to-Have Skills

• Familiarity with

  LangChain

  ,

  LlamaIndex

  , or other LLM orchestration frameworks.
• Experience building

  RAG pipelines

  for enterprise search and knowledge retrieval.
• Exposure to

  Databricks

  ,

  MLflow

  , or

  Azure DevOps

  for model management.
• Strong problem-solving and optimization skills.
• Excellent communication and collaboration abilities in a remote team environment.