Key Responsibilities

- Design and implement agentic AI workflows for clinical source verification, discrepancy detection, intelligent query generation, and related healthcare use cases.
- Build, integrate, and deploy LLM-powered AI agents using AWS Bedrock and Amazon SageMaker.
- Develop AI workflows using open-source frameworks such as LangChain, LlamaIndex, and AutoGen.
- Design and implement event-driven AI pipelines using AWS Lambda, Step Functions, and EventBridge.
- Develop and optimize prompt engineering, Retrieval-Augmented Generation (RAG), tool/function calling, and multi-agent workflows.
- Integrate AI agents with internal and external applications through secure APIs and service integrations.
- Collaborate with data engineers to design and implement secure PHI/PII-aware data ingestion and processing pipelines.
- Build mechanisms for evaluating and improving AI outputs, including accuracy, reliability, latency, and cost optimization.
- Monitor, test, debug, and fine-tune AI workflows and agent behavior in development and production environments.
- Apply appropriate security, privacy, and compliance practices when working with sensitive healthcare data.
- Stay current with emerging developments in Generative AI, LLMs, Agentic AI, and AWS AI/ML services, and evaluate their applicability to business use cases.
- Collaborate with software engineers, data engineers, and other stakeholders to translate business and clinical requirements into scalable AI solutions.

Required Qualifications

- Bachelor’s degree in Computer Science, Engineering, Artificial Intelligence, Machine Learning, or a related field.
- 3–6 years of professional experience in AI/ML engineering,



with hands-on experience developing and deploying AI/ML solutions.
- Strong hands-on experience with Generative AI, LLMs, and Agentic AI.
- Mandatory hands-on experience with AWS Bedrock and Amazon SageMaker.
- Robust programming skills in Python and/or TypeScript.
- Hands-on experience with one or more LLM/agentic AI frameworks such as LangChain, LlamaIndex, or AutoGen.
- Practical experience with RAG, prompt engineering, embeddings, vector databases, tool/function calling, and multi-agent architectures.
- Experience developing and integrating REST APIs and AI services with external systems.
- Experience with AWS services such as Lambda, Step Functions, EventBridge, S3, and related cloud services.
- Understanding of event-driven and serverless architectures.
- Experience testing, evaluating, monitoring, and optimizing AI/ML workflows for accuracy, latency, reliability, and cost.
- Strong problem-solving and analytical skills, with the ability to experiment, evaluate results, and iterate quickly.

Preferred Qualifications

- Experience developing AI solutions for Healthcare or Life Sciences.
- Familiarity with healthcare data standards, clinical workflows, or healthcare interoperability.
- Experience working with PHI/PII and sensitive data, including secure data handling and privacy controls.
- Familiarity with healthcare and AI-related regulatory and compliance requirements.

Job Types: Full-time, Permanent

Benefits:

- Flexible schedule
- Health insurance
- Paid sick time
- Paid time off
- Provident Fund
- Work from home

Application Question(s):

- Which AWS services have you had hands-on experience with?
- How many years of hands-on experience do you have with AWS AI services ?

Work Location: Remote

📌 Agentic AI Engineer (AWS Tech Stack) (India)
🏢 ZoftSolutions
📍 India