Python Engineer _ Agentic AI & MCP Orchestration

Must Have Technical/Functional SkillsTechnical Skills • Strong hands-on experience in Python for building production-grade applications. • Experience developing agentic AI applications (multi-agent workflows, tool-using agents, autonomous task execution). • Expertise with AI orchestration frameworks (agents, tools, planners, workflow controllers). • Practical experience setting up and configuring Model Context Protocol (MCP) servers. • Ability to implement and integrate MCP Clients with external systems and AI models. • Proficiency working with LLMs, prompt engineering patterns, and structured output handling. • Experience with API integration, event-driven interactions, and tool/skill registration for agents. • Strong understanding of asynchronous Python (asyncio, concurrency patterns). • Familiarity with vector stores, embeddings, RAG pipelines, and memory architectures (nice to have). • Experience working with CI/CD, Git, testing frameworks (pytest), and secure coding practices. Functional Skills • Ability to translate business problems into agent-driven automation workflows. • Strong debugging and troubleshooting of distributed agent behavior and orchestration flows. • Familiarity with governance, model safety constraints, and responsible AI usage patterns. • Strong documentation habits for API schemas, MCP interface definitions, and agent lifecycle behavior. • Effective communication with architects, product teams, and business stakeholders. • Comfort working in Agile environments with rapid experimentation and iteration. Roles Responsibilities Design and develop Python-based agentic applications capable of orchestrating autonomous and semi-autonomous workflows. • Build, configure, and maintain MCP servers to expose tools, data sources, or domain capabilities to LLM agents. • Implement and configure MCP clients to interact with multiple MCP tools, AI models, and external systems. • Develop orchestration logic to coordinate multi-agent behaviors, tool execution, validations, and decision routing. • Integrate LLMs with internal systems using structured prompts, tool definitions, and safe execution patterns. • Optimize agent workflows for reliability, performance, security, and cost. • Create reusable frameworks for agent tools, context handling, memory, and reasoning cycles. • Collaborate with architecture, platform, and product teams to align on engineering best practices. • Implement observability: logging, tracing, and monitoring of agent reasoning steps and tool calls. • Document MCP schemas, agent behaviors, tool interfaces, and operability guidelines. TCS Employee Benefits Summary: Discretionary Annual Incentive. Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans. Family Support: Maternal & Parental Leaves. Insurance Options: Auto & Home Insurance, Identity Theft Protection. Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement. Ti me Off: Vacation, Time Off, Sick Leave & Holidays. Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing. Salary Range: $100,000 - $115,000 a year

Created: 2026-03-10