Python Developer with GenAI

Responsibilities

• Accountable for leading application development supporting business objectives while demonstrating independence across software development lifecycle phases from concept and design to testing.
• Lead new and existing applications along with enhancements to applications, platforms, and infrastructure.
• Perform hands-on coding while designing and architecting secure, scalable, and high-performing data processing, analytics, GenAI, and Agentic AI solutions.
• Lead the design, architecture, deployment, and governance of enterprise-grade GenAI and Agentic AI applications that deliver measurable business value.
• Design and deploy scalable AI solutions using AWS Bedrock, AWS AgentCore, LLMs, embeddings, and enterprise AI services.
• Arcitect multi-agent workflows and agent orchestration solutions using frameworks such as AWS Strands, AutoGen, CrewAI, or similar agentic frameworks.
• Guide the implementation of advanced RAG-based solutions for enterprise knowledge retrieval, semantic search, hybrid search, contextual grounding, and business workflow automation.
• Establish evaluation and testing approaches for GenAI and Agentic AI applications to validate response quality, safety, reliability, relevance, hallucination risk, and agent performance.
• Serve as a liaison to internal customers, research groups, product teams, and business support areas to translate business needs into scalable AI solutions.
• Provide technical guidance to junior programmers and software engineers; guide design discussions, architecture reviews, and technical decision-making.
• Troubleshoot and maintain mid-level to complex applications, ensuring performance, reliability, security, and operational readiness.

Qualifications

• 8 -12 years of proven experience in software development and system maintenance.
• Strong experience in designing, architecting, and deploying GenAI and Agentic AI applications in enterprise environments.
• Experience leading the implementation of enterprise-scale GenAI solutions, ensuring they are robust, secure, scalable, and performant.
• Solid experience with Agentic AI frameworks such as AWS Strands, AutoGen, CrewAI, or similar agent orchestration frameworks.
• Strong understanding of multi-agent architectures, agent orchestration patterns, Agent-to-Agent (A2A) communication protocols, planning, reasoning, memory management, and tool integration strategies.
• Experience designing and deploying AI agent solutions using AWS AgentCore, AWS Bedrock, LLMs, embeddings, and enterprise AI services.
• Experience creating and integrating Model Context Protocol (MCP) servers, tools, and enterprise system integrations.
• Strong experience with advanced RAG architectures and retrieval techniques, including semantic search, hybrid search, contextual retrieval, vector databases, knowledge grounding, and enterprise AI search solutions.
• Experience with OpenSearch, Elasticsearch, Snowflake, embedding models, vector databases, and enterprise search platforms.
• Experience creating evaluation and testing frameworks for GenAI and Agentic AI applications, including automated evaluation, benchmarking, response quality assessment, hallucination detection, safety validation, and agent performance measurement.
• Advanced knowledge of Python and modern frameworks such as FastAPI, Flask, Django, or Django REST Framework.
• Experience working on all aspects of enterprise-scale AI implementations, including architecture, design, security, infrastructure, and GenAIOps.
• Highly experienced in developing scalable systems using software engineering best practices and design patterns.
• Strong AWS cloud experience preferred.
• Experience with C#/.NET is an added advantage.
• Highly experienced at leading teams, interacting with business partners or customers, and guiding project direction.
• Excellent understanding of object-oriented design concepts and software development processes and methodologies.
• Superior organizational skills with the ability to prioritize effectively and keep teams focused on key outcomes.
• Leadership capability to guide architecture, design, and technical discussions.
• Demonstrated ability to work independently with minimal supervision.
• Passion for software engineering, AI innovation, and emerging technologies.