AI/ML Engineer

7 hours ago

Remote, India APPIT SOFTWARE SOLUTIONS PRIVATE LIMITED Full time ₹ 12,00,000 - ₹ 36,00,000 per year

About the Role :

We are seeking an experienced AI/ML Engineer to design, develop, and deploy advanced AI-driven systems with a focus on LLM (Large Language Model) application development and AI framework architecture.

The ideal candidate will have deep expertise in backend engineering, AI orchestration, and hands-on experience with modern AI tools and frameworks.

Key Responsibilities:

- Design and implement LLM-powered applications and intelligent systems in production environments.

- Architect and deploy AI frameworks and agentic systems for scalable, real-world use cases.

- Build and orchestrate AI workflows using Spring Boot, Spring AI, and vector storage integrations.

- Develop RAG (Retrieval-Augmented Generation) pipelines and apply prompt engineering, memory management, and model orchestration techniques.

- Integrate and evaluate AI models using OpenAI, Anthropic, and Google Gemini APIs.

- Ensure strong software engineering practices - version control, CI/CD pipelines, API integrations, and deployment automation.

- Collaborate cross-functionally with data scientists, ML engineers, and backend developers to enhance model performance and reliability.

Required Skills & Experience:

years of experience in AI/ML engineering or backend systems.

- Proven track record in LLM application development and AI system design.

- Strong knowledge of the Java ecosystem, including Spring Boot and Spring AI.

- Hands-on experience with RAG architectures, prompt optimization, and vector databases (e.g., Pinecone, Weaviate, or Chroma).

- Familiarity with OpenAI, Anthropic, and Google Gemini APIs, including evaluation and reasoning quality frameworks.

- Excellent understanding of software engineering fundamentals, pipelines, CI/CD, and API integrations.

Bonus Points:

- Background in cybersecurity, SaaS platforms, or risk intelligence systems.

- Experience working in cloud-native environments and deploying scalable AI pipelines

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