Role Overview
We are seeking a highly skilled AI/ML Engineer with strong expertise in Generative AI, Large Language Models (LLMs), and workflow automation. The ideal candidate will design, develop, and deploy scalable AI-driven solutions, integrating intelligent systems into real-world business processes.
Key Responsibilities
AI/ML & Model Development
- Develop and implement machine learning and deep learning models for various use cases
- Work with supervised and unsupervised learning techniques, including model evaluation and optimization
- Train, fine-tune, and deploy ML models in production environments
LLMs & Generative AI
- Build applications using Large Language Models (LLMs), including OpenAI and open-source models (e.g., LLaMA, Mistral)
- Design and optimize prompt engineering strategies
- Develop Retrieval-Augmented Generation (RAG) systems using embeddings and vector search
- Fine-tune domain-specific AI models for business use cases
Automation & Workflow Orchestration
- Design and implement AI-driven automation workflows using tools like n8n, Zapier, or similar platforms
- Integrate AI capabilities into business processes such as sales, compliance, and security operations
- Build scalable, reliable automation systems
Backend & System Integration (Good to Have)
- Develop backend services using Python (preferred) and/or Node.js
- Build and integrate APIs (REST, GraphQL)
- Work with microservices-based architectures
Data Engineering & Pipelines
- Design and manage ETL pipelines for structured and unstructured data
- Build real-time data processing systems
- Work with vector databases such as Pinecone, Weaviate, or FAISS
MLOps & Deployment
- Deploy, monitor, and maintain ML models in production
- Implement model lifecycle management and performance tracking
- Utilize tools such as MLflow, Weights & Biases, etc.
- Work with Docker, Kubernetes, and CI/CD pipelines
Cloud & Infrastructure
- Deploy and manage AI systems on cloud platforms (AWS, Azure, or GCP)
- Ensure scalability, reliability, and performance of production systems
Required Skills & Qualifications
- Strong foundation in Machine Learning, Deep Learning, and NLP
- experience- 5-8 years
- Hands-on experience with LLMs, prompt engineering, and RAG architectures
- Experience with automation tools (n8n, Zapier, etc.)
- Proficiency in Python; Node.js is a plus
- Experience with APIs, data pipelines, and system integration
- Familiarity with vector databases and embeddings
- Knowledge of MLOps practices and deployment tools
- Experience working with cloud platforms (AWS/Azure/GCP)
Preferred Qualifications
- Experience building end-to-end AI products
- Exposure to real-time AI applications and scalable architectures
- Strong problem-solving and system design skills
