Flexday AI
Website:
flexdaysolutions.com
Job details:
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
- 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
- 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
- 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
- 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
- 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
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