Artificial Intelligence Engineer

Nxtwave Disruptive Technologies (Hiring for a client); Pune District, Chennai District, Bengaluru; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Nxtwave Disruptive Technologies (Hiring for a client) seeks engineers to build AI/LLM applications on AWS using modern agent frameworks. You will craft back-end services with FastAPI, integrate Bedrock/S3/OpenSearch, and design multi-agent architectures with tool-calling workflows.

Strong knowledge of LLMs, vector databases, and retrieval systems is required, along with hands-on Python, GitHub Actions, and API development for production-ready AI solutions.

Qualifications

• Strong hands-on experience building AI/LLM-based RAG applications and AI agents.
• Experience with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar agent frameworks.
• Experience with MCP servers and MCP client integrations.
• Proficiency in Python and backend/API development.
• Experience building services with FastAPI.
• Strong understanding of LLMs, AI agents, RAG, embeddings, rerankers, vector databases, and retrieval systems.
• Experience with prompt engineering, tool calling, structured outputs, and agent orchestration.
• Experience with foundation models such as GPT, Claude, Llama, Gemini, Amazon Nova, or equivalent.
• Experience with vector databases such as Pinecone, ChromaDB, Qdrant, Weaviate, or similar technologies.
• Experience with object/blob storage such as Amazon S3.
• Experience with search and vector search systems such as Amazon OpenSearch.
• Experience with semantic search, hybrid search, BM25, and retrieval optimization techniques.
• Experience with cross-encoders, bi-encoders, reranking models, and embedding models.
• Experience with AI observability and evaluation tools.
• Experience with Git commands.
• Experience using GitHub and GitHub Actions for version control, CI/CD, and collaboration.
• Experience with SQL and database design.
• Knowledge of cloud security, authentication, authorization, and secure AI application development.

Responsibilities

• Build AI/LLM applications on AWS using AWS services.
• Prompt engineering and evaluation frameworks
• Build AI agents using frameworks such as LangChain, LangGraph, Amazon Bedrock, AutoGen, or similar tools.
• Develop backend services and APIs using FastAPI.
• Integrate AWS services such as Amazon Bedrock, S3, Lambda, ECS/EKS, OpenSearch, and AWS database services.
• Work with foundation models such as OpenAI GPT, Anthropic Claude, Meta Llama, Amazon Nova, Gemini, or similar models.
• Design and implement AI agent architectures, multi-agent systems, and tool-calling workflows.
• Implement RAG pipelines using blob/object storage, vector search, embeddings, reranking, and document retrieval workflows.
• Build semantic search, hybrid search, and knowledge retrieval systems.
• Implement prompt engineering, prompt management, and LLM evaluation strategies.
• Develop structured output and function/tool-calling capabilities.
• Implement streaming AI responses using SSE or WebSockets.
• Implement AI application monitoring, observability, tracing, and evaluation frameworks.
• Design guardrails, content filtering, hallucination mitigation, and responsible AI controls.
• Optimize AI applications for latency, scalability, reliability, and cost.
• Work with application teams to move AI prototypes into reliable production environments.
• Implement cloud security best practices.
• Collaborate with DevOps and platform teams to deploy, monitor, and scale AIworkloads.

Skills

AI/LLM RAG apps

LangChain/LangGraph

Python backend/API

FastAPI

LLMs/agents/reranking

Prompt engineering

Vector databases

S3/storage

OpenSearch/vector search

Git/GitHub Actions

SQL/database design

Tools

AWS

Docker

Kubernetes

Job description

Location

Pune, Gurgaon, Mohali, Panchkula, Dehradun, Bangalore, Mumbai, Chennai

Check Points

1. VERY STRONG communication skills

3. No cab provision to employee from compan

Required Skills

• Strong hands-on experience building AI/LLM-based RAG applications and AI agents.


• Experience with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar agent frameworks.


• Experience with MCP servers and MCP client integrations.


• Proficiency in Python and backend/API development.


• Experience building services with FastAPI.


• Strong understanding of LLMs, AI agents, RAG, embeddings, rerankers, vector databases, and retrieval systems.


• Experience with prompt engineering, tool calling, structured outputs, and agent orchestration.


• Experience with foundation models such as GPT, Claude, Llama, Gemini, Amazon Nova, or equivalent.


• Experience with vector databases such as Pinecone, ChromaDB, Qdrant, Weaviate, or similar technologies.


• Experience with object/blob storage such as Amazon S3.


• Experience with search and vector search systems such as Amazon OpenSearch.


• Experience with semantic search, hybrid search, BM25, and retrieval optimization techniques.


• Experience with cross-encoders, bi-encoders, reranking models, and embedding models.


• Experience with AI observability and evaluation tools.


• Experience with Git commands.


• Experience using GitHub and GitHub Actions for version control, CI/CD, and collaboration.


• Experience with SQL and database design.


• Knowledge of cloud security, authentication, authorization, and secure AI application development.

Responsibilities

• Build AI/LLM applications on AWS using AWS services.


• Prompt engineering and evaluation frameworks


• Build AI agents using frameworks such as LangChain, LangGraph, Amazon Bedrock, AutoGen, or similar tools.


• Develop backend services and APIs using FastAPI.


• Integrate AWS services such as Amazon Bedrock, S3, Lambda, ECS/EKS, OpenSearch, and AWS database services.


• Work with foundation models such as OpenAI GPT, Anthropic Claude, Meta Llama, Amazon Nova, Gemini, or similar models.


• Design and implement AI agent architectures, multi-agent systems, and tool-calling workflows.


• Implement RAG pipelines using blob/object storage, vector search, embeddings, reranking, and document retrieval workflows.


• Build semantic search, hybrid search, and knowledge retrieval systems.


• Implement prompt engineering, prompt management, and LLM evaluation strategies.


• Develop structured output and function/tool-calling capabilities.


• Implement streaming AI responses using SSE or WebSockets.


• Implement AI application monitoring, observability, tracing, and evaluation frameworks.


• Design guardrails, content filtering, hallucination mitigation, and responsible AI controls.


• Optimize AI applications for latency, scalability, reliability, and cost.


• Work with application teams to move AI prototypes into reliable production environments.


• Implement cloud security best practices.


• Collaborate with DevOps and platform teams to deploy, monitor, and scale AIworkloads.

Nice to Have

• Experience with Node.js and TypeScript.


• Experience with Amazon Bedrock, SageMaker, Azure OpenAI, OpenAI APIs, Anthropic APIs, or Google Vertex AI.


• Experience containerizing and deploying applications using Docker and Kubernetes.


• Experience with AI observability platforms such as Langfuse, Arize Phoenix, Helicone, or similar tools.


• Experience with model evaluation frameworks such as Ragas, DeepEval, LangSmith, or similar tools.


• Experience building multi-agent systems and autonomous workflows.


• Knowledge of enterprise security, compliance, governance, and cost optimization practices.


• Experience deploying production-grade AI solutions on AWS.


• Familiarity with MLOps, LLMOps, CI/CD, and Infrastructure as Code (Terraform, CloudFormation).