AI developer

Key Responsibilities

• Design, develop, and maintain AI/ML applications and intelligent systems.
• Build and integrate Generative AI and LLM-based solutions into enterprise platforms.
• Develop APIs, microservices, and backend systems for AI workflows.
• Fine-tune, evaluate, and optimize machine learning models for performance and scalability.
• Work with vector databases, embeddings, prompt engineering, and RAG architectures.
• Collaborate with product managers, QA teams, and developers to deliver AI features.
• Integrate third-party AI platforms such as OpenAI, Azure OpenAI, AWS AI, or Google Vertex AI.
• Ensure data quality, security, and compliance in AI pipelines.
• Monitor model performance and continuously improve AI systems in production.
• Participate in architecture discussions, code reviews, and technical mentoring.
• Troubleshoot and resolve AI-related production issues.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
• 5+ years of software development experience with 2+ years in AI/ML development.
• Strong proficiency in Python.
• Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Hands-on experience with Generative AI, LLMs, prompt engineering, and RAG pipelines.
• Experience working with OpenAI APIs or similar AI platforms.
• Strong knowledge of REST APIs, microservices, and backend development.
• Experience with vector databases such as Pinecone, Weaviate, FAISS, or ChromaDB.
• Familiarity with cloud platforms like AWS, Azure, or Google Cloud.
• Experience with Docker, Kubernetes, and CI/CD pipelines.
• Good understanding of SQL/NoSQL databases.
• Strong problem-solving and communication skills.

Preferred Qualifications

• Experience building AI copilots, chatbots, or AI search platforms.
• Knowledge of NLP, embeddings, semantic search, and recommendation systems.
• Experience with LangChain, LlamaIndex, or AI orchestration frameworks.
• Familiarity with MLOps practices and model deployment pipelines.
• Experience in staffing, recruitment, HR tech, or SaaS platforms is a plus.