Generative AI Engineer

Key Responsibilities

1. Solution Architecture & Deployment

• Design and deploy scalable, secure GenAI architectures integrated into customer-facing products.
• Build REST APIs for AI/ML models and deploy them in containerized environments (Docker, Kubernetes) on cloud platforms (AWS, Azure, GCP).

1. GenAI & LLM Development

• Fine-tune and optimize generative models including GPT, VAEs, GANs, and transformer-based architectures.
• Apply techniques like Retrieval-Augmented Generation (RAG) and prompt engineering to enhance model performance and relevance.
• Work with both commercial and open-source LLMs (e.g., GPT-4, Claude, LLaMA 3.2, Phi).

1. Agentic AI Integration

• Primary Focus: Build, deploy, and optimize AI agents leveraging frameworks such as LangChain, LangGraph, CrewAI, AgentFlow, and Autogen.
• Implement orchestration strategies, multi-agent collaboration, tool integration, and memory/state management.
• Drive experimentation to create autonomous or semi-autonomous agents that solve real business workflows and decision-making processes.

1. MLOps & Performance Optimization

• Establish MLOps pipelines covering model lifecycle: training, CI/CD, monitoring, and retraining.
• Use tools like Git, Docker, Kubernetes, and vector DBs to ensure efficient and reliable deployment.
• Optimize resource utilization and infrastructure costs.

1. Cross-Functional Collaboration

• Partner with engineering, data science, and product teams to align technical solutions with business goals.
• Effectively communicate complex concepts across diverse technical and non-technical audiences.
• Stay current with industry advancements and drive innovation in GenAI and AI agent strategy.

Skills & Qualifications

Required

• Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain).
• Hands-on experience in building and deploying AI agents with orchestration, tool use, and state management.
• In-depth knowledge of LLM architecture, RAGs, embeddings, prompt tuning, and vector databases, agentic AI patterns (ReAct, tool-calling agents, multi-step reasoning, guardrails)
• Experience with cloud platforms (AWS, Azure, GCP) and containerization.
• Strong analytical, problem-solving, and communication skills.
• Data integration experience — REST APIs, Google APIs, SQL databases. Comfortable moving data between systems.
• Experience in Web development: FastAPIs, Typescript, async patterns, building production APIs, React, node.js, Component architecture, hooks, state management, consuming streaming APIs (SSE/WebSocket)

Preferred

• 4+ years of hands-on experience with LLMs and GenAI in production settings.
• Exposure to agentic AI tools and multi-agent workflows (e.g., CrewAI, LangGraph, Autogen).
• Familiarity with MLOps and AI deployment best practices.
• Experience in client-facing or cross-functional AI initiatives.
• Publications, open-source contributions, or demonstrable projects showcasing AI agent development.