Generative AI Engineer / Artificial Intelligence Engineer

Location – Remote

Shift -3PM – 12AM

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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.