The ideal candidate will work closely with client engineering and product teams to design, develop, integrate, and deploy production-grade agentic AI solutions. This role requires strong software engineering fundamentals, the ability to work with modern AI frameworks, and excellent client-facing problem-solving skills.
Design and develop scalable Agentic AI applications and AI-powered solutions using Python.
Build, integrate, and optimize AI agents, multi-agent workflows, tools, and orchestration pipelines.
Develop production-grade Python services, APIs, integrations, and backend components.
Work with LLMs, prompt engineering, RAG, embeddings, vector databases, and tool/function calling.
Implement agent workflows using frameworks such as LangChain, LangGraph, OpenAI Agent SDK, Google ADK, CrewAI, AutoGen, or similar frameworks.
Integrate AI agents with enterprise systems, APIs, databases, SaaS platforms, and business applications.
Develop and consume REST APIs, microservices, and event-driven integrations.
Implement appropriate mechanisms for agent memory, context management, state management, and knowledge retrieval.
Work with cloud-based AI platforms such as AWS Bedrock, Azure AI Foundry, or Google Cloud Vertex AI/Gemini.
Implement observability, monitoring, logging, evaluation, and performance optimization for AI applications.
Collaborate with architects, product managers, data scientists, and client stakeholders to translate business requirements into technical solutions.
Participate in client discussions, technical workshops, solution demonstrations, and proof-of-concepts.
Troubleshoot complex technical issues and provide hands-on engineering support during implementation.
Follow secure and responsible AI engineering practices, including appropriate authentication, authorization, data protection, and AI governance.
Experience with Docker, Kubernetes, CI/CD, Git, and cloud deployment.
Exposure to AI observability and evaluation tools.
Understanding of AI governance, guardrails, responsible AI, and security considerations for agentic systems.
Experience with MCP (Model Context Protocol) and enterprise tool integrations.
Experience working with enterprise-grade AI platforms or agent orchestration platforms.
Knowledge of authentication and authorization mechanisms such as Oauth2/JWT.
Experience working in financial services, banking, or other highly regulated environments is a plus.
Never miss an opportunity! Create an alert based on the job you applied for.
