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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.