AI / GenAI Engineer / NLP

Role & responsibilities

• Design, prototype, and deploy

  AI-driven applications

  leveraging LLMs (GPT-4, Perplexity, Claude, Gemini, etc.) and open-source transformer models.
• Lead or co-lead

  end-to-end AI/GenAI solutions

  : from data ingestion, entity extraction, and semantic search to user-facing interfaces.
• Implement

  RAG (Retrieval-Augmented Generation)

  architectures, knowledge grounding pipelines, and prompt orchestration logic.
• Fine-tune transformer models (BERT, RoBERTa, T5, LLaMA) on custom datasets for use cases like:

Document understanding

Conversational AI

Question answering

Summarization & Topic Modeling

• Integrate LLM workflows into

  scalable backend architectures

  with APIs and frontends.
• Work closely with business teams and pharma SMEs to

  translate requirements into GenAI solutions

  .
• Mentor junior engineers and contribute to

  AI capability development

  across the organization.

Tech Stack & Skills Required

• Programming & Libraries

  : Python, FastAPI, LangChain, Pandas, PyTorch/TensorFlow, Transformers (HuggingFace), OpenAI SDK.

• Data Extraction & Processing

  : PDFMiner, PyMuPDF, Tabula, PyPDF2, Tesseract OCR, python-pptx.

• Gen AI / LLMs

  : OpenAI (GPT), Gemini, Perplexity, Cohere, DeepSeek, Mistral, LLaMA, BERT, RoBERTa, T5, Falcon.

• Use Cases

  : NER, Summarization, QA, Document Parsing, Clustering, Topic Modeling,

QA over docs.

• Embedding & Vector Databases

  : Pinecone, FAISS, ChromaDB.

• RAG & Retrieval Pipelines

  : LangChain, Haystack, custom retrievers.

• Frontend/Backend Integration

  : React (preferred), FastAPI/Flask, REST/GraphQL APIs.

• Versioning & Deployment

  : Git, Docker, CI/CD (basic), cloud knowledge is a plus (AWS/GCP/Azure).

Preferred candidate profile

• Degree in Computer Science, Engineering, Data Science, or related field (BE/BTech/MTech/MCA).
• 35 years of hands-on experience in

  AI/ML/NLP/LLM solution development.

• Strong understanding of

  GenAI, Prompt Engineering, LLM internals

  , and

  multi-layered data architectures.

• Exposure to

  pharma/healthcare domain

  is a significant plus.
• Excellent problem-solving skills, self-learner, and ability to work in cross-functonal teams.
• Experience developing new applications within an agile environment preferred.
• Ability to work independently and as part of a team.
• Exposure to MLOPs will be an added advantage.