AI Integration Lead for Genomics & Healthcare

Talent21 Management and Shared Services Pvt Ltd; Bahrain; On-site; BHD 9,400 - 17,000; Full time

Job summary

Talent21 Management and Shared Services Pvt Ltd. seeks an experienced AI Integration Specialist to lead deployment of ML models, pharmacogenomics predictions, conversational chatbots, and vector search systems for a large-scale national genomic dataset.

You will build and deploy models with TensorFlow, PyTorch, and Scikit-learn; implement RAG architectures, vector DBs like Pinecone/FAISS/Weaviate, and integrate LLMs such as GPT.

Qualifications

• Bachelor's degree in IT, CS, AI or related field.
• 3+ years of hands-on ML/AI deployment experience.
• Experience with TensorFlow, PyTorch, Python, and Scikit-learn.
• Familiarity with vector databases and LLM integrations.
• Experience with ASP.NET Core, RESTful APIs, and microservices.

Responsibilities

• Lead deployment of ML models, pharmacogenomics predictions, chatbots, and vector search systems.
• Build, fine-tune, and deploy ML/DL models using TensorFlow, PyTorch, and Scikit-learn.
• Implement production-grade RAG architectures and LLM integrations.
• Architect cloud-native AI pipelines on AWS services (SageMaker, Bedrock, NextFlow, Lambda, S3, IAM).
• Design RESTful APIs and integrate backend services in .NET/ASP.NET Core enterprises.
• Process large healthcare/genomics datasets.

Skills

3+ years experience

Strong communication

Healthcare/genomics domain

Education

Bachelor's degree in IT/CS/AI

Tools

TensorFlow

PyTorch

Python

Scikit-learn

AWS SageMaker

AWS Bedrock

NextFlow

Pinecone

FAISS

Weaviate

ASP.NET Core

RESTful APIs

Docker

MLOps

Job description

Talent21 Management and Shared Services Pvt Ltd. seeks an experienced AI Integration Specialist to lead deployment of ML models, pharmacogenomics predictions, conversational chatbots, and vector search systems for a large-scale national genomic dataset.

You will build and deploy models with TensorFlow, PyTorch, and Scikit-learn; implement RAG architectures, vector DBs like Pinecone/FAISS/Weaviate, and integrate LLMs such as GPT.