Job Responsibilities

End-to-End AI Application Development: Design, build, and deploy full-stack artificial intelligence applications from the ground up, seamlessly connecting robust backend AI services with responsive, user-friendly frontends.
AI Model Integration & Orchestration: Integrate Large Language Models (LLMs), local model pipelines, vector databases, and multi-agent orchestration frameworks (such as RAG architectures) into production-ready software systems.
Database Architecture & Performance Optimization: Architect, query, and optimize complex relational databases (PostgreSQL preferred), writing advanced SQL queries, multi-table joins, cursors, and stored procedures to guarantee high-performance data retrieval and storage.
Backend Engineering & APIs: Develop high-performance, asynchronous backend services, RESTful APIs, and data ingestion pipelines primarily using Python to handle real-time data streaming and complex business logic.
UI/UX & Dashboard Development: Build intuitive, responsive user interfaces and real-time operational dashboards using modern web frameworks to enable seamless human-AI interaction and telemetry visualization.
Containerization & DevOps: Package applications using Docker and manage deployment, scaling, and cluster coordination using Kubernetes to ensure enterprise-grade reliability and fault tolerance.
Cloud & Infrastructure Management: Provision, configure, and optimize cloud resources (primarily AWS) and high-performance computing environments (including GPU clusters) to support heavy AI workloads.
Linux System Operations: Operate effectively within Linux environments, utilizing advanced shell utilities, system monitoring, and performance tuning for backend stability.
Team Mentorship & Onboarding: Actively mentor, train, and guide junior developers and team members under your purview, rapidly bringing them up to speed on technical frameworks, coding standards, and best practices.
Stakeholder Communication & Live Demos: Communicate effectively with diverse internal and external stakeholders, translating complex technical concepts into clear business value, and confidently conduct live, end-to-end software and AI application demonstrations.
Quality & Continuous Improvement: Apply a quality-first mindset (incorporating principles like Lean Six Sigma where applicable) to debug issues, optimize system latency, automate testing, and ensure high reliability.
Technology Exploration & Innovation: Stay on the cutting edge of AI advancements, rapidly evaluating, prototyping, and integrating emerging open-source tools, frameworks, and model updates into the enterprise stack.

Desired Criteria :

We are looking for a candidate who combines deep technical expertise with a strong quality-first mindset. Specifically, the ideal candidate should have hands-on experience with:

Core Technical Stack: Advanced Python development, AWS cloud infrastructure, and enterprise Linux administration.
DevOps & Deployment: Expertise in containerization (Docker) and orchestration (Kubernetes) for large-scale enterprise deployments.
AI & Innovation: Proven ability to build and deploy modern AI solutions (Multi-agent systems, RAG) and a passion for quickly adopting emerging technologies.
Quality & Process: A background in Lean Six Sigma and other quality tools to ensure operational excellence and high-reliability software.

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Lead Full-Stack Developer

Full Stack AI Lead Developer