Urgent Requirement AI ML Developer Healthcare AI

Kamineni Hospitals; Hyderabad; Vor Ort; INR 1.800.000 - 2.400.000; Vollzeit

Zusammenfassung

Kamineni Hospitals is seeking an AI/ML Developer to design, develop, and deploy AI/ML and Generative AI solutions for healthcare. You will work on LLM-driven applications, RAG pipelines, and tool-calling systems, collaborating with a full-stack team to integrate AI into hospital workflows and patient care platforms.

The role emphasizes hands-on ML with LLMs, fine-tuning, embeddings, and deployment on hospital systems. Strong Python/PyTorch experience and English communication are expected.

Qualifikationen

• Educational background in Computer Science or related AI/ML field.
• Hands-on experience with LLMs with practical projects.
• Experience with fine-tuning and deployment of AI models.

Aufgaben

• Design and develop LLM-powered healthcare AI applications and agents.
• Experiment with model architectures, prompts, and inference strategies.
• Build production-ready RAG systems and vector-store pipelines.
• Develop AI capabilities to integrate with hospital workflows and dashboards.
• Collaborate with full-stack team to expose AI via APIs.
• Ensure evaluation, testing, and monitoring of AI solutions.

Kenntnisse

LLM APIs

Open-source LLMs

Hugging Face

Python

PyTorch

TensorFlow

Vector databases

FastAPI

Docker

NLP

Ausbildung

Bachelor's or Master's in CS/AI/ML

Tools

FAISS

Qdrant

Milvus

Weaviate

Elasticsearch/OpenSearch

llama.cpp

Jobbeschreibung

Role Overview

We are looking for an AI/ML Developer to join our healthcare AI initiative and work on the design, development, experimentation, evaluation, and deployment of AI/ML and Generative AI solutions for healthcare. This role is specifically focused on AI/ML engineering, rather than conventional web or full-stack development. The candidate will work closely with the existing full-stack development team to build AI capabilities that can be integrated into hospital applications, workflows, dashboards, and patient/clinical systems. The ideal candidate should have hands‑on experience with LLMs, RAG, model fine‑tuning, LoRA/QLoRA, embeddings, vector databases, prompt engineering, model evaluation, and AI inference/deployment.

Key Responsibilities

1. Generative AI & LLM Development

• Design and develop LLM-powered applications for healthcare use cases. Work with commercial and open-source LLMs. Evaluate different models for accuracy, latency, cost, multilingual capabilities, and healthcare use cases. Develop AI agents, assistants, conversational systems, and domain-specific AI applications. Implement structured prompting, function/tool calling, structured outputs, and agentic workflows. Experiment with different model architectures and inference approaches.

2. LLM Fine-Tuning

Hands‑on experience in fine‑tuning LLMs is strongly preferred. The candidate should have practical experience with: LoRA (Low‑Rank Adaptation) QLoRA Parameter‑efficient fine‑tuning (PEFT) Supervised Fine‑Tuning (SFT) Instruction tuning Dataset preparation and cleaning Training/validation dataset creation Model checkpointing and evaluation Quantization Hugging Face Transformers ecosystem Experience fine‑tuning models such as Llama, Qwen, Mistral, Gemma, Phi, or equivalent open‑source models is desirable. Candidates should be able to demonstrate at least one hands‑on LLM fine‑tuning project, rather than only theoretical knowledge.

3. RAG & Knowledge Systems

Design and implement production‑quality Retrieval‑Augmented Generation (RAG) systems. Experience with: Document ingestion and preprocessing Chunking strategies Embeddings Vector databases Semantic search Hybrid search Metadata filtering Reranking Retrieval evaluation Context management Hallucination reduction Citation/grounding mechanisms Experience with technologies such as FAISS, Qdrant, Weaviate, Milvus, pgvector, Chroma, Elasticsearch/OpenSearch, or equivalent is desirable.

4. AI Agents & Tool Calling

Develop AI systems capable of interacting with external tools and hospital systems. Experience with: Function/tool calling API integration Agent workflows Multi‑step reasoning workflows Structured JSON outputs Workflow orchestration State/session management Guardrails Human‑in‑the‑loop workflows Examples include AI systems capable of interacting with hospital APIs, retrieving patient information, checking appointments, generating reports, or assisting hospital staff. 5. Machine Learning

Strong understanding of fundamental ML concepts including: Supervised and unsupervised learning Classification Regression Clustering Feature engineering Model training and validation Model evaluation Cross‑validation Overfitting/underfitting Precision, recall, F1, ROC‑AUC and other relevant metrics Experience with scikit‑learn, XGBoost, PyTorch and/or TensorFlow is desirable.

5. AI Evaluation & Quality

Develop systematic methods to evaluate AI systems before and after deployment. Work on: Accuracy evaluation Hallucination measurement Retrieval evaluation LLM‑as‑a‑judge approaches Benchmark datasets Prompt/model comparison Regression testing for AI responses Latency and cost measurement Safety and reliability testing Multilingual evaluation The candidate should understand that AI development requires continuous evaluation and experimentation, not simply connecting an API to an application.

6. Healthcare AI

Work with clinical and operational teams to identify and develop AI applications across areas such as: Clinical decision support Patient communication Hospital chatbots Voice AI Medical information retrieval Clinical documentation Hospital MIS and analytics Patient experience Operational automation Predictive analytics Medical coding / classification Healthcare workflow automation The candidate should be able to work carefully with healthcare data and understand the importance of privacy, security, accuracy, explainability, and human oversight.

7. Speech & Multimodal AI

Experience in one or more of the following is an advantage: Speech‑to‑text Text‑to‑speech Voice agents Multilingual AI Telugu / Indian-language AI Audio processing Vision‑language models OCR Medical image AI Multimodal LLMs Experience building Indian‑language or multilingual AI applications will be highly valued.

8. Model Deployment & MLOps

Support deployment of AI models into production environments. Experience with: Model serving REST APIs FastAPI Docker GPU‑based inference CPU inference optimization Quantization vLLM / Ollama / llama.cpp or equivalent inference frameworks Hugging Face ecosystem Model versioning Experiment tracking Monitoring and logging Understanding of cloud GPU infrastructure and model deployment is desirable.

9. Collaboration with Full‑Stack Development Team

The organization already has a dedicated full‑stack development team. The AI/ML Developer will: Build AI/ML components and services. Expose AI functionality through APIs. Work with full‑stack developers for application integration. Define API contracts and AI service requirements. Support integration of AI capabilities into existing hospital applications. Debug AI‑related issues jointly with the application development team. The role should remain primarily focused on AI/ML rather than routine frontend/backend application development.

Required Technical Background

LLM / GenAI

• LLM APIs
• Open‑source LLMs
• Hugging Face

Transformers

LoRA

• QLoRA

PEFT

• SFT
• Prompt engineering
• Function/tool calling RAG

Embeddings

• Vector databases
• Semantic search
• Hybrid search
• Reranking
• RAG evaluation ML
• Python
• PyTorch
• TensorFlow
• scikit‑learn
• NumPy / Pandas Deployment

FastAPI

• Docker
• GPU inference
• vLLM / Ollama / llama.cpp
• Cloud AI/GPU platforms Mandatory / Preferred Experience Mandatory
• Strong Python programming skills.
• Practical experience developing AI/ML solutions.

Understanding of machine learning fundamentals.

• Experience working with LLMs or Generative AI.
• Experience with data preparation, experimentation, and model evaluation. Strongly Preferred

Hands‑on experience fine‑tuning LLMs using LoRA / QLoRA / PEFT.

• Experience building production RAG systems.

Experience with open‑source LLMs such as Llama, Qwen, Mistral, Gemma, Phi, or equivalent.

Experience deploying or serving AI models.

Experience with vector databases.

• Experience building AI agents/tool‑calling systems.
• Experience with speech, multilingual, or multimodal AI. Advantage

Healthcare AI experience.

• Medical NLP experience.
• Experience with Indian languages / Telugu.
• Experience with medical or clinical datasets.
• Experience with GPU infrastructure.

Published research, GitHub projects, Kaggle projects, papers, or demonstrable AI projects.

Educational Qualification

• Bachelor's or Master's degree in:

Computer Science Artificial Intelligence / Machine Learning

Data Science Computer Engineering

Information Technology Mathematics / Statistics

or a related technical discipline.

Candidates with strong practical AI/ML portfolios may be considered irrespective of specialization.

Experience Recommended:

25 years of relevant AI/ML experience.

Candidates with less experience but strong hands‑on projects in LLM fine‑tuning, RAG, AI agents, or model deployment may also be considered.

What We Look For

We are particularly interested in candidates who can build and experiment, rather than candidates whose experience is limited to consuming AI APIs. Candidates should be able to demonstrate projects involving one or more of:

Fine‑tuning an open‑source LLM using LoRA/QLoRA

• Building a production or advanced RAG pipeline
• Training or deploying an ML model Building an AI agent with tool/function calling
• Deploying an LLM locally or on GPU infrastructure
• Building a multilingual/voice AI system
• Developing an AI evaluation framework

Working with real‑world, noisy datasets Candidates may be asked to demonstrate a previous AI project or complete a practical AI/ML assessment as part of the selection process.

Key Performance Areas

• Development and deployment of AI/ML solutions
• Successful LLM/RAG experimentation and implementation
• Model accuracy and evaluation quality
• Successful fine‑tuning and optimization of models
• AI prototype‑to‑production conversion
• AI system performance, latency, and cost optimization

Integration of AI services with hospital applications

• Development of reusable AI components and services
• Contribution to new healthcare AI use cases

Continuous evaluation of emerging AI technologies