Generative AI Engineer

Job Title: Data Scientist RAG & Edge AI Systems

Location: Chennai

Experience: 3+ years

Joining: Immediate to 30 days preferred

About the Role

Data Scientist

hands-on engineering

Key Responsibilities

• Design and develop

  LLM and RAG-based systems

  that enhance knowledge discovery, automation, and decision support.
• Build and orchestrate

  end-to-end AI pipelines

  using

  Azure AI Studio

  ,

  Logic Apps

  , and

  Data Factory

  .
• Develop APIs and microservices for AI model integration with business and plant systems.
• Optimize and deploy deep learning models to

  edge devices

  (e.g.,

  NVIDIA Jetson

  ,

  AI PCs

  ) for real-time inference.
• Fine-tune LLMs for

  domain-specific tasks

  , balancing accuracy, speed, and compute efficiency.
• Collaborate cross-functionally with

  data engineers, software developers, and domain experts

  to bring AI prototypes to production.
• Monitor model performance, automate retraining pipelines, and ensure compliance with

  data governance

  and

  security standards

  .

Required Skills & Experience

• RAG & LLMs:

  Experience building Retrieval-Augmented Generation systems using frameworks such as

  LangChain

  ,

  LlamaIndex

  , or

  Azure OpenAI Service

  .

• Azure Stack:

  Hands-on experience with

  Azure AI Studio

  ,

  Azure Machine Learning

  ,

  Logic Apps

  ,

  Functions

  , and

  Data Factory

  .

• Programming:

  Strong proficiency in

  Python

  , with experience in

  PyTorch

  ,

  TensorFlow

  ,

  LangChain

  , and

  FastAPI

  .

Edge AI Processing:

• Deploying and optimizing models on edge devices

  such as

  NVIDIA Jetson

  ,

  Intel AI PCs

  , or

  ARM-based systems

  .
• Proficiency in

  TensorRT

  for real-time inference.
• Familiarity with

  Docker

  and

  remote monitoring

  for distributed edge workloads.

• Data Handling:

  Experience with

  SQL

  and

  vector databases

  for retrieval and embedding management.

• Visualization & Monitoring:

  Experience building

  dashboards

  using

  Power BI

  ,

  Streamlit

  , or similar tools for inference metrics and explainability.

Preferred / Nice-to-Have

• Strong understanding of

  vector search optimization

  ,

  document embeddings

  , and

  semantic retrieval

  at scale.
• Experience with

  multimodal AI systems

  (text and image) for industrial or enterprise use.
• Understanding of

  model compression

  ,

  quantization

  , and

  GPU optimization

  for on-device AI.
• Experience integrating AI models with

  manufacturing systems

  (MES, SCADA, or PLC data).
• Familiarity with

  Elixir/Phoenix

  for chatbot or API integration within enterprise platforms.
• Awareness of

  AI safety, ethical AI

  , and

  data privacy frameworks

  in regulated environments.