Senior Full Stack AI Engineer

Evoke Technologies; Hyderabad; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Evoke Technologies seeks an AI/Cloud Platform Engineer to advance AI-powered services in Hyderabad. You will design and implement RAG pipelines, agentic workflows and robust LLM integrations across backend services. The role emphasizes secure deployments, observability and rapid iteration within an Agile framework.

Ideal candidates have hands-on experience with AWS Bedrock, LLMs, and modern orchestration tools, with a focus on production-grade AI systems and responsible guardrails.

Qualifications

• Hands-on experience with AWS Bedrock and LLM architectures.
• Experience implementing retrieval-augmented generation (RAG) systems.
• Designing agentic workflows and multi-agent orchestration.
• Proficiency across the LLMOps lifecycle and automated evaluation.
• Skills to implement AI guardrails, secure integrations and data protection.

Skills

AWS Bedrock

LLM architectures

foundational models

RAG systems

prompt engineering

agentic workflows

LangGraph

LlamaIndex

tool calling

workflow routing

structured task decomposition

LLMOps lifecycle

prompt evaluation

production observability

tracing & debugging

AI guardrails & security

prompt injection mitigation

secure AI integrations

Java

Spring Boot

Python

serverless deployments

AWS Lambda

ECS/Fargate

CI/CD pipelines

GitHub Actions

Maven/Gradle

Bamboo

infrastructure as code

JSON/HTTP

API authentication

MySQL

PostgreSQL

Oracle

MongoDB

DynamoDB

unit testing

integration testing

performance profiling

production monitoring

GitHub Copilot

Amazon Q Developer

AWS Kiro

Cursor

JetBrains AI

VS Code AI

Agile Scrum

Job description

• AI & Cloud Platforms: Hands-on experience with AWS Bedrock; familiarity with LLM architectures and foundational models.
• RAG & Prompt Engineering: Proficiency implementing retrieval-augmented generation (RAG) systems and crafting high-quality prompts for LLM interactions.
• AI Orchestration / Agentic Workflows: Proven experience designing and implementing agentic workflows and multi-agent systems using modern orchestration frameworks (e.g., LangGraph, LlamaIndex), including tool calling, workflow routing, and structured task decomposition.
• LLMOps / Evaluation & Observability: Strong experience across the LLMOps lifecycle, including automated prompt/model evaluation and benchmarking (e.g., promptfoo), and production observability, tracing, and debugging
• AI Governance / Guardrails & Security: Demonstrated ability to implement AI guardrails and safety controls to mitigate prompt injection, prevent sensitive data leakage, enforce policy-based outputs, and ensure secure AI integrations in production environments.
• Backend Systems: Experience with Java, Spring Boot, Python, for building backend services.
• Cloud & DevOps: Familiarity with serverless and containerized deployments (e.g., AWS Lambda, ECS/Fargate), CI/CD pipelines (GitHub Actions, Maven, Gradle, Bamboo), and infrastructure as code.
• Data & APIs: Comfortable integrating AI models with data sources, microservices, and external APIs; experience with JSON, HTTP, and API authentication patterns.
• Databases: Hands-on development experience using RDBMS/SQL databases (e.g., MySQL, PostgreSQL, Oracle) and NoSQL databases (e.g., MongoDB, DynamoDB), including schema design, query optimization, and integration with web applications and AI-driven services.
• Testing & Quality: Experience writing automated tests (unit, integration), performance profiling, and production monitoring.
• AI-Assisted Development Tools: Hands-on experience using AI development assistants (e.g., GitHub Copilot, Amazon Q Developer, AWS Kiro, Cursor, JetBrains AI, VS Code AI extensions) to accelerate development, improve code quality, and support productivity in day-to-day engineering workflows.
• Agile / Product Delivery: Demonstrated experience delivering software in an Agile Scrum / product delivery environment, including story estimation, sprint execution, backlog refinement, collaboration with Product Owners, and continuous improvement practices.