Key

Responsibilities

Leadership & Strategy * Lead architectural design and implementation of multi-agent AI systems * Drive technical strategy for GenAI initiatives and recommend best practices * Mentor and provide technical guidance to junior and mid-level engineers * Collaborate with stakeholders to define requirements and deliver solutions * Own end-to-end delivery of complex, production-scale AI systems Technical Execution * Build and maintain high-performance REST/WebSocket APIs using FastAPI (Pydantic v2) * Implement and optimize agentic AI systems using frameworks like LangGraph, Deep Agents, AutoGen, and LangChain * Architect real-time, event-driven microservices using messaging queues like Apache Kafka * Design clean, testable, maintainable services using SOLID principles, Python async, and type hints * Integrate and optimize SQL, NoSQL, and vector databases (Postgres, MongoDB, ChromaDB, Pinecone) * Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls, backed by LLM observability and evaluation tooling (e.g., LangSmith, Langfuse) * Apply LLM safety guardrails (prompt-injection mitigation, PII handling, content moderation) in line with Banking, Insurance, and Healthcare compliance requirements * Stay current with emerging trends in GenAI, deep learning, and AI orchestration framework Skills and Competencies * Proven ability to architect and deliver end-to-end GenAI solutions and multi-agent systems * Strong software engineering discipline: testing (unit, integration, performance), code review, documentation * Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders * Strategic thinking and problem-solving with a focus on scalability and maintainability * Leadership capability: mentoring, technical guidance, and cross-functional collaboration RESPONSIBILITIES

Key Responsibilities

Leadership & Strategy * Lead architectural design and implementation of multi-agent AI systems * Drive technical strategy for GenAI initiatives and recommend best practices * Mentor and provide technical guidance to junior and mid-level engineers * Collaborate with stakeholders to define requirements and deliver solutions * Own end-to-end delivery of complex, production-scale AI systems Technical Execution * Build and maintain high-performance REST/WebSocket APIs using FastAPI (Pydantic v2) * Implement and optimize agentic AI systems using frameworks like LangGraph, Deep Agents, AutoGen, and LangChain * Architect real-time, event-driven microservices using messaging queues like Apache Kafka * Design clean, testable, maintainable services using SOLID principles, Python async, and type hints * Integrate and optimize SQL, NoSQL, and vector databases (Postgres, MongoDB, ChromaDB, Pinecone) * Run LangGraph/Deep Agents workflows in production with checkpointing, persistence, and human-in-the-loop controls, backed by LLM observability and evaluation tooling (e.g., LangSmith, Langfuse) * Apply LLM safety guardrails (prompt-injection mitigation, PII handling, content moderation) in line with Banking, Insurance, and Healthcare compliance requirements * Stay current with emerging trends in GenAI, deep learning, and AI orchestration framework Skills and Competencies * Proven ability to architect and deliver end-to-end GenAI solutions and multi-agent systems * Strong software engineering discipline: testing (unit, integration, performance), code review, documentation * Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders * Strategic thinking and problem-solving with a focus on scalability and maintainability * Leadership capability: mentoring, technical guidance, and cross-functional collaboration QUALIFICATIONS Minimum

Qualifications

* Bachelor's degree in Computer Science, Data Science, or related field * 5+ years of total professional experience, including: * 3+ years of hands-on Software Engineering experience in Python, FastAPI and relevant tech stack * 2+ years working specifically with GenAI and LLMs (GPT, Claude, LLaMA, etc.) * Track record of shipping production ML/AI products, with strong prompt-engineering skills, systems-level thinking, and the ability to diagnose and resolve production failures * Strong software engineering background with expertise in OOP and SOLID principles * Proficiency in Python 3.11+ (async/await, type hints, modern Python patterns, strict type checking) * Experience with agentic frameworks (LangChain, LangGraph, AutoGen, or similar) * Proven track record building production REST/WebSocket APIs and microservices * Experience with message streaming platforms (Kafka, Pulsar, or similar) * Strong knowledge of databases: SQL, NoSQL, and vector databases * Working knowledge of RAG pipelines (embeddings, chunking, retrieval) and LLM observability/evaluation tools (LangSmith, Langfuse, or similar)