AI Engineer

#### Location: Remote ( Preferable if the candidate location is Bengaluru or Gurgaon)

About the Role

We are looking for talented engineers who combine strong communication, stakeholder management, and hands-on expertise in AI agents. You should be equally comfortable presenting a solution to a Fortune 500 business sponsor, working through ambiguous requirements with product managers, and building production-grade agentic workflows in code. A sense of ownership, continuous learning, and genuine curiosity about the rapidly evolving agent ecosystem are essential.

At Flexday AI, you will design, build, and deploy production-grade Agentic AI solutions for large enterprise clients. We are a multi-cloud (AWS, Azure, GCP) and multi-LLM (OpenAI, Azure OpenAI, Anthropic, Gemini) AI solutions firm, and you will work with global teams across the full development lifecycle, from discovery to production.

Key Responsibilities

• Partner with client stakeholders, product owners, and business leads to understand requirements, shape solutions, and communicate progress clearly and credibly
• Design and develop Agentic AI solutions deployed at enterprise scale
• Translate functional and business requirements into technical solutions in collaboration 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

Required Skills

1. Communication and Stakeholder Management (Primary Requirement)

• Excellent written and verbal communication skills in English
• Demonstrated ability to explain technical concepts to non-technical business stakeholders
• Comfort facilitating working sessions, leading solution walkthroughs, and managing expectations with client sponsors
• Strong sense of ownership, accountability, and follow-through
• Ability to operate independently in ambiguous, fast-moving environments

2. Agentic AI (Primary Technical Requirement)

• Solid working knowledge of AI agents, agentic workflows, and the patterns behind them, such as tool use, planning, memory, and multi-agent orchestration
• Hands-on experience building agents using frameworks such as LangGraph, LangChain, OpenAI Agents SDK, Semantic Kernel, or equivalent (professional or personal projects both count)
• Understanding of how to evaluate, debug, and productionize agent behavior in enterprise settings
• Familiarity with Model Context Protocol (MCP) servers and multi-agent orchestration patterns is strongly preferred

3. AI Specialization (Deep Expertise in at Least One Area)

• LLM and Generative AI: prompt engineering, RAG, fine-tuning, and LLM integration
• Machine Learning: classical ML, feature engineering, model training, evaluation, and deployment
• Computer Vision: CNNs, detection, segmentation, vision transformers, or OCR

4. Programming and Software Engineering

• 2 to 5+ years of hands-on software development experience, primarily in Python
• Proven contribution to large-scale programs deployed in enterprise environments
• Understanding of full-stack development, REST APIs, and microservices
• Disciplined approach to code quality, testing, and documentation

5. Cloud and Data

• Hands-on experience developing and deploying on AWS or Azure (GCP is a plus)
• Experience working with large datasets and data pipelines
• Familiarity with Docker and Git

Good to Have

Enterprise Platforms

• Amazon Bedrock and Amazon Bedrock AgentCore
• Microsoft Copilot and Copilot Studio
• Azure OpenAI, Azure AI Foundry, or Google Vertex AI

Agent Frameworks and Protocols

• LangGraph, LangChain, OpenAI Agents SDK, or similar
• Model Context Protocol (MCP) servers and multi-agent orchestration

RAG and Retrieval Systems

• Vector databases (Pinecone, Weaviate, Qdrant, pgvector)
• Embedding pipelines, semantic search, and document intelligence

Data Engineering

* Databricks, PySpark, Microsoft Fabric, Synapse, or similar platforms, with a good working understanding of the underlying concepts

DevOps and MLOps

• Understanding of CI/CD, model deployment, monitoring, and observability
• Hands-on experience with any industry-leading MLOps tools and platforms

Qualifications

• Bachelor’s or Master’s degree in computer science, Engineering, Data Science, or a related technical field from a reputed institution.
• A PhD or equivalent qualification is highly valued, and candidates with doctoral backgrounds are encouraged to apply