AI Engineer

Caprus It; Bengaluru; On-site; INR 1,800,000 - 2,600,000; Full time

Job summary

Caprus It in Bengaluru (India) is seeking a GenAI Solutions Architect to design and deliver enterprise-grade Generative AI applications, translating business problems into prompt-driven, retrieval-augmented AI solutions.

You will integrate with enterprise data platforms, implement evaluation, guardrails and monitoring for production deployments, and leverage Databricks, FAISS, AWS Bedrock, Azure OpenAI and other cloud AI services to build scalable GenAI workflows.

Qualifications

• Hands-on designing and deploying Generative AI solutions on AWS and Azure, including Azure OpenAI, AWS Bedrock, and Azure Machine Learning.
• Strong expertise in prompt engineering, RAG architectures, embeddings, and vector databases such as Databricks Vector Search and FAISS.

Responsibilities

• Develop and optimize prompt engineering strategies, including prompt templates, chaining, tool use (agents), and grounding techniques (RAG).
• Build and deploy LLM-powered applications using enterprise data, integrating with APIs, vector databases, and knowledge systems.
• Implement evaluation, monitoring, and guardrails for GenAI systems, addressing hallucinations, bias, response quality, and performance in production.

Skills

Prompt engineering

RAG architectures

Embeddings

Vector databases

API integration

CI/CD for GenAI

Governance and monitoring

Tools

Azure OpenAI

AWS Bedrock

Azure Machine Learning

Databricks

Spark

Delta Lake

FAISS

Databricks Vector Search

GitHub

Azure DevOps

Job description

PURPOSE AND SCOPE

Design and deliver Generative AI solutions (LLMs, copilots, agents) that enhance automation, knowledge access, and decision-making across the enterprise.

Translate business problems into prompt-driven and retrieval-augmented (RAG) AI applications integrated with enterprise data platforms.

Ensure responsible, secure, and scalable GenAI implementations, including governance, evaluation, and monitoring of model behavior.

PRINCIPAL DUTIES AND RESPONSIBILITIES

Develop and optimize prompt engineering strategies, including prompt templates, chaining, tool use (agents), and grounding techniques (RAG).

Build and deploy LLM-powered applications using enterprise data (structured/unstructured), integrating with APIs, vector databases, and knowledge systems.

Implement evaluation, monitoring, and guardrails for GenAI systems, addressing hallucinations, bias, response quality, and performance in production.

EXPERIENCE AND REQUIRED SKILLS

Hands-on experience designing and deploying Generative AI solutions on AWS and Azure, including services such as Azure OpenAI, AWS Bedrock, and Azure Machine Learning.

Strong expertise in prompt engineering, RAG architectures, embeddings, and vector databases (e.g., Databricks Vector Search, FAISS), with experience grounding LLMs in enterprise data.

Proficiency with Databricks (Spark, Delta Lake) for data preparation and integration with GenAI workflows, along with Azure DevOps and GitHub for CI/CD and GenAI application lifecycle management.

Experience with AI/LLM evaluation frameworks, including prompt/response evaluation, hallucination detection, safety filtering, and benchmark-driven optimization to ensure high-quality and reliable outputs.