Senior AI Engineer
Clinisys · Bengaluru, IN ·
- Seniority
- Senior
- Category
- AI engineering
- Experience
- 4+ years
- Company size
- 501-1000
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Develops and maintains cloud-based AI agents and RAG pipelines on Azure, mentors engineers, and ensures production stability for healthcare diagnostic platforms.
About the Role
We are seeking an experienced, self-driven Senior AI & Agent Developer with 4 to 6 years of experience to join our engineering team in a Hybrid model. In this role, you will act as a core Individual Contributor, owning the end-to-end lifecycle of our cloud-based AI initiatives.
You will heavily leverage tools like GitHub Copilot to accelerate development speed, while designing, building, and maintaining multi-agent systems and advanced Retrieval-Augmented Generation (RAG) pipelines natively on the Azure Cloud platform using Azure AI Foundry. Beyond code development, you will take full operational support ownership of live agents and act as a technical mentor to upskill and train our existing engineering team on cloud-native AI practices.
Clinisys' AI Philosophy
Building an AI‑first organisation is central to Clinisys’ purpose and the impact we deliver. As a global provider of intelligent diagnostic informatics solutions, we build AI‑enabled, cloud‑based platforms to enhance diagnostic workflows across healthcare, life sciences, and public health. By applying intelligent technology thoughtfully and responsibly, we help laboratories and testing environments operate more effectively, generate meaningful insights at scale, and ultimately support healthier and safer communities. Operating across more than 30 countries, Clinisys expects all colleagues—regardless of role or function—to work confidently with AI‑enabled tools, apply digital and analytical thinking, and continuously adapt as technologies evolve. We must drive an AI-first sense of purpose and urgency.
Key Responsibilities
1. AI Agent Development & Core Engineering
Architect and Build: Design and develop production-grade multi-agent frameworks and enterprise LLM applications using Python.
Cloud-Native AI: Build, deploy, and evaluate models directly within the Azure AI Foundry ecosystem.
Advanced RAG Pipelines: Implement complex Retrieval-Augmented Generation (RAG) systems, integrating advanced semantic search, chunking strategies, and re-ranking techniques.
Vector Infrastructure: Set up and manage vector databases (such as Azure AI Search or specialized vector stores) for efficient embedding storage and high-speed similarity searches.
AI-Assisted Coding: Maximize daily development efficiency and code quality by effectively utilizing GitHub Copilot.
2. Agent Maintenance & Production Support
Agent Operations (AgentOps): Actively monitor, debug, and provide production support for live autonomous AI agents to ensure runtime stability and minimal downtime.
Performance Optimization: Track token usage, optimize context windows, and implement robust guardrails within Azure to prevent agent hallucinations or infinite loops.
System Integration: Connect AI agent workflows with enterprise databases, Azure storage, and external third-party APIs.
3. Team Upskilling & Knowledge Sharing
Technical Training: Conduct structured workshops and hands-on coding sessions to elevate the AI capabilities of our internal engineering team.
Best Practices: Teach team members how to effectively prompt, use GitHub Copilot, and leverage Azure's AI suite.
Required Technical Skills
Programming Language: Absolute mastery of Python (object-oriented design, asynchronous programming, and clean code principles).
Cloud AI Ecosystem: Deep, hands-on experience using Azure AI Foundry (formerly Azure AI Studio) to manage, prompt, and evaluate LLMs.
Cloud Infrastructure: Strong, hands-on proficiency with Azure Cloud Services (e.g., Azure App Services, Azure Functions, Azure Container Instances) making it seamless to deploy and manage backend code.
AI Frameworks & Vector Tools: Experience with orchestration frameworks (such as LangChain, CrewAI, AutoGen, or LlamaIndex) and vector databases (such as Azure AI Search, Pinecone, or Qdrant).
AI Tooling: Native workflow familiarity with GitHub Copilot for accelerated coding and code review.
Qualifications & Soft Skills
Experience: 4 to 6 years of professional software development experience, with a significant focus on cloud-native AI/ML engineering.
Autonomy: Proven track record as an independent Individual Contributor who can take an AI product from a cloud concept to live production support with minimal supervision.
Communication: Exceptional communication and presentation skills, with a clear passion for teaching, mentoring, and training other developers.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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