Applied AI Engineer

Location: Hyderabad, India
Employment Type: Full-Time
Experience: 411 Years

About InfoMagnus

InfoMagnus is building the next generation of enterprise AI solutions through our Precision AI Frameworka comprehensive approach that combines methodologies, software accelerators, governance, and platforms to maximize business value from Applied AI.

As an Applied AI Engineer, you'll work alongside AI Architects and global consulting teams to design and build production-grade AI systems for enterprise customers. This is a hands-on engineering role where you'll leverage cutting-edge LLM technologies, agentic AI frameworks, AI-assisted development tools, and modern cloud platforms to deliver scalable, secure, and observable AI solutions.

About the Role

We're looking for engineers who love building production AI systemsnot just prototypes.

You'll develop enterprise-grade AI applications powered by Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic workflows, and modern AI infrastructure. Working in an AI-native engineering environment, you'll use tools such as GitHub Copilot, Claude Code, Cursor, LangChain, CrewAI, LangGraph, and OpenAI Agents SDK to accelerate development and deliver robust, production-ready solutions.

Key Responsibilities:

AI Application Development

- Design, develop, and deploy LLM-powered applications using LangChain, LlamaIndex, Semantic Kernel, and similar frameworks.
- Build Retrieval-Augmented Generation (RAG) pipelines using vector databases such as Pinecone, Qdrant, Milvus, or pgvector.
- Develop conversational AI, AI assistants, and multi-agent systems using CrewAI, LangGraph, or OpenAI Agents SDK.
- Implement prompt engineering strategies, tool calling, function calling, and workflow orchestration.
- Build reusable AI accelerators, templates, and components aligned with the Precision AI Framework.

AI-Augmented Engineering

- Use GitHub Copilot, Claude Code, Cursor, or similar AI coding assistants as part of your daily development workflow.
- Drive AI-assisted software development practices, including automated testing, AI-powered code reviews, and intelligent CI/CD automation.
- Evaluate emerging AI engineering tools and establish engineering best practices.

Data Engineering & AI Infrastructure

- Build enterprise data pipelines supporting AI workloads.
- Develop embedding pipelines, chunking strategies, metadata enrichment, and retrieval optimization.




- Deploy AI applications on AWS, Azure, or GCP using Docker, Kubernetes, and Infrastructure as Code (Terraform, CDK, or Pulumi).
- Implement MLOps practices including experiment tracking, model versioning, evaluation, and deployment automation.

Observability, Governance & Security

- Implement AI observability using OpenTelemetry, LangSmith, Langfuse, Arize Phoenix, or similar platforms.
- Build governance capabilities including guardrails, policy enforcement, audit logging, and compliance automation.
- Apply AI security best practices including prompt injection protection, PII handling, secrets management, and secure model access.

Collaboration & Delivery

- Work closely with AI Architects and consulting teams throughout the solution lifecycle.
- Participate in architecture discussions, code reviews, sprint planning, and technical design sessions.
- Create technical documentation, deployment guides, and operational runbooks.
- Mentor team members and contribute reusable engineering assets.

Required Skills & Experience:

- 4+ years of software engineering experience with at least 2 years building AI/ML or data-intensive applications.
- Solid Python programming skills.
- Hands-on experience with LangChain, LlamaIndex, Semantic Kernel, or equivalent LLM orchestration frameworks.
- Experience integrating OpenAI, Anthropic, Azure OpenAI, or open-source LLMs.
- Strong understanding of prompt engineering, function calling, streaming, and AI workflow design.
- Experience with Retrieval-Augmented Generation (RAG), embeddings, vector databases, and semantic search.
- Hands-on experience using GitHub Copilot, Claude Code, Cursor, or similar AI-assisted development tools.
- Experience deploying containerized applications using Docker and Kubernetes.
- Cloud experience with AWS, Azure, or Google Cloud Platform.
- Infrastructure as Code experience using Terraform, CDK, Pulumi, or equivalent.
- Experience with MLflow, Weights & Biases, Neptune, or similar MLOps platforms.
- Knowledge of observability and monitoring using OpenTelemetry or AI observability platforms.




- Strong communication and technical documentation skills.

Preferred Qualifications:

- Experience building multi-agent AI systems using CrewAI, LangGraph, AutoGen, or OpenAI Agents SDK.
- Experience with ETL/ELT pipelines, Kafka, Flink, Spark, or modern data engineering platforms.
- Knowledge of AI governance, compliance, and responsible AI practices.
- Experience implementing guardrails, prompt injection protection, content filtering, and PII detection.
- Familiarity with React and TypeScript for AI-powered user interfaces.
- Contributions to open-source AI projects or technical publications.
- Consulting or client-facing delivery experience.
- Bachelor's or Master's degree in Computer Science, AI/ML, or a related discipline.

Technology Stack:

AI Development: GitHub Copilot, Claude Code, Cursor

LLM Frameworks: LangChain, LlamaIndex, Semantic Kernel

Agentic AI: CrewAI, LangGraph, OpenAI Agents SDK

Vector Databases: Pinecone, Qdrant, Milvus, pgvector

MLOps: MLflow, Weights & Biases, Neptune

Observability: OpenTelemetry, LangSmith, Langfuse, Arize Phoenix

Governance & Security: NeMo Guardrails, Lakera Guard, HiddenLayer

Cloud & Infrastructure: AWS, Azure, GCP, Docker, Kubernetes, Terraform

During the interview, candidates should be prepared to discuss:

- A GenAI/LLM project involving LangChain or LlamaIndex, including RAG implementation, prompt engineering, and vector databases such as Pinecone or Qdrant.
- Experience building agentic AI systems using CrewAI, LangGraph, OpenAI Agents SDK, or similar frameworks.
- Deploying AI applications on AWS, Azure, or GCP using Docker, Kubernetes, Terraform, and CI/CD pipelines.
- MLOps and AI observability using MLflow, Weights & Biases, OpenTelemetry, LangSmith, Langfuse, or similar tools.
- AI security and governance practices including prompt injection prevention, guardrails, PII protection, content filtering, and responsible AI implementation.

Apply Now

Send your updated resume along with one of the following:

- A GitHub repository showcasing your AI/ML work
- A short write-up describing a production AI solution you've built
- An example demonstrating how you've used AI coding assistants (GitHub Copilot, Claude Code, Cursor, etc.) to accelerate software development

Email: [email protected]

We're looking for engineers who love building, experimenting, and shipping impactful AI solutions.

📌 Senior Artificial Intelligence Engineer (Hyderabad)
🏢 InfoMagnus
📍 Hyderabad