AI & Full Stack Engineer

: AI & Full Stack Developer

Location: India Currently remote, with potential transition to a hybrid/on-site model in the future

Employment Type: Full-time

We are looking for an experienced high-impact AI & Full Stack Engineer who can design, build, and deploy production-grade AI systems across Generative AI and enterprise automation use cases. This role goes beyond experimentation—you will own end-to-end AI solutions, from model development to scalable deployment across cloud and edge environments. The ideal candidate is someone who has worked on .NET, Nodejs and who can translate ambiguous business problems into robust AI solutions, optimize models for real-world constraints and integrate AI seamlessly into enterprise workflows.

Key Responsibilities

1. AI Solution Architecture & Delivery

• Design and implement end-to-end AI systems spanning data ingestion, model development, inference, and deployment
• Build scalable solutions for document intelligence, fraud detection, and automation workflows
• Own production deployments with a focus on reliability, performance, and maintainability.

2. Generative AI & LLM Engineering

• Develop and deploy RAG-based systems, conversational AI, and multi-agent workflows
• Fine-tune and optimize LLMs for domain-specific use cases
• Integrate LLMs with enterprise systems using APIs and orchestration frameworks (e.g.LangChain)
• Implement prompt engineering, evaluation pipelines, and guardrails.

3. Computer Vision & Intelligent Systems (Desirable)

• Design and deploy real-time computer vision pipelines (OCR, object detection, face recognition,
• video analytics)
• Build AI-driven solutions for industrial inspection, surveillance, and document processing
• Optimize models for edge devices and high-throughput environments.

4. Backend & AI System Integration

• Develop high-performance AI services using FastAPI/Flask and REST-based architectures
• Integrate AI models with databases and enterprise systems for real-time decision-making
• Build scalable APIs supporting AI workloads.

5. Deployment, MLOps & Optimization

• Deploy models using containerized environments and CI/CD pipelines
• Manage deployments across AWS, Azure, and hybrid edge-cloud environments
• Monitor model performance and continuously improve accuracy and efficiency.

6. Leadership & Collaboration

• Work closely with cross-functional teams (product, engineering, business)
• Mentor junior engineers and contribute to AI best practices
• Drive technical decisions and architecture discussions.

Technical Qualifications and Experience

1. AI & Machine Learning

• Strong experience in Computer Vision (Desirable) and Generative AI systems
• Expertise in RAG, LLM fine-tuning, multi-agent systems, and vector databases
• Hands-on with frameworks such as PyTorch, TensorFlow, Hugging Face, Lang Chain.

2. Backend & AI Services

• Strong experience in Python-based backend development (FastAPI, Flask)
• Proven ability to build REST APIs and scalable AI services
• Experience with databases such as MongoDB, MySQL, PostgreSQL.

3. Deployment & Infrastructure

• Experience deploying AI systems on AWS (EC2, Lambda), Azure, or hybrid environments
• Strong understanding of model serving, CI/CD, and containerized deployments.

4. Additional Skills

• Experience building document intelligence or fraud detection systems
• Strong problem-solving skills and ability to work on complex, ambiguous requirements
• Experience working in production AI environments at scale.

5. Experience Requirements

• Minimum 5 years of software development experience in enterprise application development.
• 2–3 years of hands-on AI development experience, including Generative AI, LLMs, RAG systems, or intelligent automation solutions.
• Strong experience in full stack/backend development using technologies such as .NET, React, Node.js, or other frontend/backend frameworks.
• Solid understanding of Software Development Life Cycle (SDLC), system design, and enterprise-grade application development practices.
• Experience building and deploying applications on Azure or AWS cloud platforms is REQUIRED for this role.
• Experience integrating AI capabilities into scalable production applications and enterprise systems.