Sr Software Engineer

AI Platform Development

• Build and maintain AI platform services enabling agentic capabilities across product suite
• Implement domain agents for specific business workflows (proposal generation, compliance checking, data analysis)
• Develop integration layers connecting legacy products to AI orchestration platform
• Build APIs and SDKs for product teams to consume AI services
• Optimize LLM prompts and fine-tune models for domain-specific tasks

Multi-Agent System Implementation

• Implement agent coordination logic using LangGraph and orchestration frameworks
• Build agent memory systems and context management for long-running workflows
• Develop tool-calling infrastructure for agents to interact with enterprise systems
• Implement semantic routing for efficient agent selection and task delegation
• Create agent monitoring dashboards and debugging tools

Orchestration Platform Maintenance

• Maintain existing orchestration coding platform (6-phase engine: analysis → integration)
• Enhance WebSocket infrastructure for real-time agent-to-user communication
• Optimize Git/SVN integration for programmatic repository operations
• Debug and resolve cross-platform compatibility issues
• Improve error handling and recovery mechanisms for agent workflows

ML/AI Team Collaboration

• Work with ML engineers on model deployment and inference optimization
• Collaborate with data scientists on feature engineering and training data quality
• Implement A/B testing frameworks for prompt variations and model comparisons
• Build data pipelines for collecting agent interaction data for fine-tuning
• Integrate new models and capabilities as ML team delivers them

RAG & Knowledge Management

• Build and maintain vector database infrastructure for enterprise knowledge
• Implement GraphRAG patterns for complex document relationships
• Optimize chunking strategies and embedding generation pipelines
• Develop hybrid search combining vector, keyword, and graph queries
• Build knowledge ingestion pipelines for diverse data sources

Performance & Reliability

• Optimize API latency and reduce orchestration overhead (target: <3s)
• Implement caching strategies for embeddings and LLM responses
• Build cost monitoring and optimization for LLM API usage
• Design circuit breakers and fallback mechanisms for external API failures
• Implement comprehensive logging and tracing for debugging

Technical Expertise

• 5+ years software engineering experience with 1-2+ years in AI/ML systems
• Strong Python proficiency: FastAPI, asyncio/await patterns, LangChain, LangGraph fundamentals
• TypeScript/React development: React 18+, hooks (useState, useEffect, useContext), WebSocket clients
• LLM API integration: OpenAI API, Anthropic Claude API, streaming responses, error handling
• Vector database experience: implementation and query optimization
• Understanding of multi-agent systems and workflow orchestration patterns
• Experience building scalable REST APIs and real-time WebSocket services

AI/ML Knowledge

• Practical experience with prompt engineering and LLM response optimization
• RAG implementation: chunking strategies, embedding generation, retrieval optimization, hybrid search
• Agent development: LangGraph workflows, tool calling, agent memory, state management
• Model deployment and integration (GPT-4, Claude, Llama, Phi-3)
• Understanding of MLOps practices: A/B testing, prompt versioning, model monitoring
• Experience with embedding models (OpenAI, Amazon Titan, sentence-transformers)
• Knowledge of semantic caching and context window management

Software Engineering

• Backend development: FastAPI or Flask, async Python, RESTful API design
• Frontend development: React, TypeScript, component architecture, state management
• Database skills: PostgreSQL or MySQL - schema design, query optimization, indexing
• Real-time communication: WebSocket protocol, session management, broadcasting patterns
• Authentication: OAuth2, JWT tokens, session management, RBAC implementation
• Version control: Git workflows, branching strategies, merge conflict resolution, Git worktrees, SVN etc
• Testing: pytest, unittest, integration testing, mocking LLM responses
• Understanding of containerization (Docker) and cloud deployment basics

Collaboration & Problem-Solving

• Ability to collaborate effectively with ML engineers, data scientists, and product teams
• Strong debugging skills for distributed systems and async workflows
• Experience integrating third-party APIs and handling rate limits/retries
• Clear technical communication for code reviews, documentation, and knowledge sharing
• Comfortable with agile methodologies (Scrum, Kanban) and iterative development
• Self-directed with ability to break down complex problems into manageable tasks