Home/Jobs/AI Engineer (India) AI Engineer (India) Flexday AI Gurugram 2-5 years Today $20.5K–33.7K/yr Full time Remote Skills Required LLM Gen AI RAG LangChain OpenAI Agents SDK Agentic AI agentic workflows Prompt Engineering Fine-tuning Azure OpenAI LangGraph Semantic Kernel Model Context Protocol (MCP) Machine Learning Computer Vision Description Flexday AI is hiring an AI Engineer in India to design, build, and deploy production-grade agentic AI solutions for enterprise clients. The role combines strong stakeholder communication with hands-on engineering across cloud and AI agent workflows.

Company: Flexday AI Role: AI Engineer (India) Location: Remote (Preferable if the candidate location is Bengaluru or Gurgaon) Experience 2 to 5+ years of hands-on software development experience, primarily in Python

Proven contribution to large-scale programs deployed in enterprise environments

Hands-on experience developing and deploying on AWS or Azure

Strong sense of ownership, accountability, and follow-through

Ability to operate independently in ambiguous, fast-moving environments Qualification Bachelor’s degree in computer science, Engineering, Data Science, or a related technical field from a reputed institution

Master’s degree in computer science, Engineering, Data Science, or a related technical field from a reputed institution

PhD or equivalent qualification Responsibilities Partner with client stakeholders, product owners, and business leads to understand requirements and shape solutions

Communicate progress clearly and credibly to stakeholders

Design and develop Agentic AI solutions deployed at enterprise scale





Translate functional and business requirements into technical solutions with product and business teams

Build, test, and deploy AI components on AWS, Azure, or GCP

Take end-to-end ownership of features from development through production

Collaborate with remote, cross-functional global teams across multiple time zones

Present solutions to business sponsors and lead solution walkthroughs

Work through ambiguous requirements with product managers

Build production-grade agentic workflows in code Additional Responsibilities Facilitate working sessions with client sponsors

Manage expectations with client sponsors

Evaluate, debug, and productionize agent behavior in enterprise settings

Work across the full development lifecycle from discovery to production Nice To Have Familiarity with Model Context Protocol (MCP) servers

Familiarity with multi-agent orchestration patterns

GCP experience

Amazon Bedrock

Amazon Bedrock AgentCore

Microsoft Copilot

Copilot Studio

Azure OpenAI

Azure AI Foundry

Google Vertex AI

Pinecone

Weaviate

Qdrant pgvector

Embedding pipelines semantic search document intelligence

Databricks

PySpark

Microsoft Fabric

Synapse

Understanding of CI/CD model deployment monitoring observability

Hands-on experience with industry-leading MLOps tools and platforms A PhD or equivalent qualification More Skills English communication,



stakeholder management, tool use, planning, memory, multi-agent orchestration, LLM integration, classical ML, feature engineering, model training, evaluation, deployment, CNNs, detection, segmentation, vision transformers, OCR, Python, full-stack development, REST APIs, microservices, code quality, testing, documentation, AWS, Azure, GCP, large datasets, data pipelines, Docker, Git Other Multi-cloud environment spanning AWS, Azure, and GCP

Multi-LLM environment spanning OpenAI, Azure OpenAI, Anthropic, and Gemini

Global teams

Enterprise clients

Continuous learning and curiosity about the evolving agent ecosystem are essential Follow for Daily AI Tutorials Prepare for this role Recommended resources to build the skills for this position. Sponsored. Generative AI with Large Language Models Coursera Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment. Functions, Tools and Agents with LangChain Coursera Advanced LangChain covering function calling, tool use, and conversational agents.

Deep Learning Specialization Coursera

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