Generative AI Software Engineer

We are seeking a high-caliber Generative AI Software Engineer to architect, build, and deploy production-grade Artificial Intelligence solutions and autonomous agentic systems. In this role, you will bridge the gap between cutting-edge foundational models (LLMs) and resilient, scalable enterprise software.

You will work closely with cross-functional product teams, engineering leads, and business stakeholders to translate complex domain challenges into intelligent, production-ready AI products. The ideal candidate pairs deep technical competency in Agentic AI systems (LangChain, LangGraph, Multi-Agent Orchestration) with battle-tested Full-Stack Engineering (Node.js/TypeScript, Python, React) and modern cloud-native infrastructure (AWS, Docker, Microservices).

If you are passionate about pushing beyond basic prompt wrappers to design sophisticated multi-agent architectures, self-correcting agentic loops, and scalable RAG pipelines, this role is built for you.

Key Responsibilities:

GenAI & Agentic Solution Architecture

• Architect Multi-Agent Systems: Design, prototype, and productionize collaborative multi-agent workflows using frameworks such as LangGraph and LangChain, enabling autonomous agents to execute complex, multi-step business processes.
• Implement Protocol Integrations: Deploy modular frameworks like the Model Context Protocol (MCP) and tool-calling interfaces to seamlessly interface LLMs with internal microservices, enterprise databases, and third-party APIs.
• Build Advanced RAG Architectures: Engineer high-throughput Retrieval-Augmented Generation pipelines incorporating semantic search, hybrid retrieval, dense vector embeddings, dynamic re-ranking, and vector databases (e.g., Pinecone, MongoDB Atlas Vector Search).
• AI Reliability & Governance: Develop deterministic evaluation loops, guardrails, and self-critique mechanisms to mitigate hallucinations, manage context windows efficiently, control token expenditure, and guarantee brand/policy alignment.

Full-Stack & Microservices Software Engineering

• Enterprise API & Backend Development: Build performant, secure, and resilient microservices and RESTful/WebSocket APIs using Python (FastAPI) and Node.js/TypeScript.
• Modern Web Frontend Integration: Deliver intuitive, responsive user interfaces and interactive dashboards using React, Next.js, and modern CSS frameworks (TailwindCSS) to showcase real-time AI generation, human-in-the-loop validation, and streaming responses.
• Data Pipelines & Storage: Design and manage scalable schemas across relational and NoSQL/Vector databases, including PostgreSQL, MongoDB, DynamoDB, and Redis.

Cloud Infrastructure, MLOps & Production Engineering

• Cloud Architecture (AWS): Deploy, monitor, and scale AI microservices on AWS leveraging services such as ECS, EKS, Lambda, S3, SQS/SNS, API Gateway, and CloudWatch.
• Containerization & CI/CD: Containerize microservices using Docker and orchestrate deployment via Kubernetes and automated CI/CD pipelines (GitHub Actions / Bitbucket Pipelines).
• Production Support & Incident Triage: Provide tier-3 production support, perform root-cause analysis on system failures or model drift, and maintain 99.9% platform availability.

Innovation, Agile Delivery & Mentorship

• Rapid Prototyping to MVP: Utilize Agile and Lean engineering methodologies to rapidly deliver proofs-of-concept (POCs) and iterate to Minimum Viable Products (MVPs).
• Continuous Tech Scouting: Continuously evaluate emerging foundational models (OpenAI GPT-4/o-series, Google Gemini/Vertex AI, Anthropic Claude, open-weights models) and agentic frameworks for commercial viability.
• Technical Documentation & Code Quality: Author clear architectural decision records (ADRs), API specifications, and system runbooks while fostering rigorous peer code reviews.

Required Experience:

• Professional Background: 3+ years of professional software engineering experience, with at least 1–2+ years dedicated to developing and deploying Generative AI and LLM-powered applications in production environments.
• Demonstrated AI Portfolio: Tangible experience having designed, built, and shipped production AI systems, such as:
• Autonomous or semi-autonomous multi-agent workflows (LangGraph/LangChain).
• Production RAG search engines with vector databases.
• AI-powered assistants, interactive copilot chatbots, or automated content-generation platforms with human-in-the-loop controls.
• Full-Stack Proficiency: Strong dual-competency across Python (FastAPI) and TypeScript/Node.js, with proven ability to craft UI frontends in React.
• Cloud & Microservices: Hands-on experience deploying containerized workloads (Docker) on cloud platforms, preferably AWS.
• Education: Bachelor's degree in Computer Science, Software Engineering, Information Technology, or equivalent practical industry experience.

Preferred / Value-Add Qualifications:

• Familiarity with enterprise agent standards such as Anthropic's Model Context Protocol (MCP) or Google Agent Development Kit (ADK).
• Experience with Audio/Speech AI services (Speech-to-Text, Text-to-Speech, Whisper, Polly, ElevenLabs).
• Relevant AWS Certifications (e.g., AWS Certified Developer Associate, AWS Solutions Architect).
• Background in InsurTech, FinTech, Contact Center automation, or enterprise customer experience platforms.