Description

Key Responsibilities

• Design and develop AI‑powered applications using machine learning, generative AI, and data‑driven services.
• Integrate ML models, LLMs, and AI services into web, mobile, and enterprise applications.
• Build production‑grade APIs and microservices to serve AI predictions and insights.
• Collaborate with data scientists and ML engineers to operationalize models.
• Implement prompt engineering, model orchestration, and inference pipelines.
• Ensure performance, scalability, security, and reliability of AI applications.
• Work on real‑time and batch AI inference use cases.
• Implement observability and monitoring for AI behavior and application health.
• Handle model versioning, rollback strategies, and A/B testing.
• Ensure compliance with data privacy, responsible AI, and governance standards.
• Participate in architecture reviews and contribute to AI application best practices.
• Troubleshoot application and inference issues in production environments.
• Mentor junior developers and contribute to technical documentation.

Required Skills & Qualifications

Application Development

• 5–6 years of experience in application development or software engineering.
• Strong proficiency in Python and/or JavaScript/TypeScript.
• Experience with backend frameworks (FastAPI, Flask, Django, Node.js).
• Strong understanding of REST APIs, microservices, and system design.
• Experience with frontend frameworks is a plus (React, Angular, Vue).

AI & Machine Learning

• Hands‑on experience integrating ML models and AI services into applications.
• Understanding of ML lifecycle, inference patterns, and model usage.
• Experience with Generative AI / LLMs (OpenAI, Azure OpenAI, AWS Bedrock, Hugging Face).
• Knowledge of prompt engineering, context management, and RAG (Retrieval‑Augmented Generation).
• Familiarity with embeddings and vector search.

Data & Backend Integration

• Strong SQL skills and experience with databases (relational & NoSQL).
• Experience integrating with data pipelines, feature stores, and analytics systems.
• Knowledge of APIs, caching layers, and messaging systems (Kafka, RabbitMQ).

Cloud & DevOps

• Hands‑on experience with cloud platforms (AWS / Azure / GCP).
• Experience deploying AI applications using Docker & Kubernetes.
• Familiarity with CI/CD pipelines.
• Experience with cloud‑native AI services is a plus

Key Skills

Education

Bachelor's degree