Generative AI Engineer / Artificial Intelligence Engineer

Location – Remote

Shift -3PM – 12AM

Job Description:

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).

2. 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).

3. 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.

4. 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.

5. 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.