FULLTIME
PRESCIENCE - AI Engineer
Prescience Decision Solutions (A Movate Company)
Not specified · onsite · Posted 57d ago
Sourced from
Not disclosed4–6 yrsfulltimeNot specified
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Section · 01
About this role
Job Description
Key Responsibilities :
Solution Architecture & Deployment
Key Responsibilities :
Solution Architecture & Deployment
- Design and deploy secure, scalable GenAI architectures integrated into applications.
- Build and deploy REST APIs for AI/ML models.
- Work with Docker, Kubernetes in cloud environments (AWS/Azure/GCP).
- Fine-tune and optimize LLMs (GPT, VAEs, GANs, transformer-based models).
- Implement RAG pipelines, embedding, and prompt engineering techniques.
- Work with commercial and open-source LLMs (GPT, Claude, LLaMA, Phi).
- Build and deploy AI agents using LangChain, LangGraph, CrewAI, Autogen, AgentFlow.
- Implement multi-agent systems, orchestration, tool integration, and state management.
- Develop autonomous or semi-autonomous workflows for business use cases.
- Set up end-to-end MLOps pipelines (CI/CD, monitoring, retraining).
- Optimize performance, scalability, and infrastructure costs.
- Use tools like Git, Docker, Kubernetes, vector databases.
- Develop APIs using FastAPI / Node.js.
- Work with React, TypeScript, async patterns, WebSockets/SSE.
- Handle data integration using REST APIs, SQL, and external systems.
- Partner with Engineering, Product, and Data teams.
- Communicate complex AI concepts clearly to technical and non-technical stakeholders.
- Stay updated with the latest advancements in GenAI and AI agents.
- Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain).
- Hands-on experience with LLMs, RAG, embedding, and prompt tuning.
- Experience building AI agents and multi-agent systems.
- Experience with cloud platforms (AWS/Azure/GCP) and containerization.
- Strong knowledge of REST APIs and data integration.
- Experience with FastAPI, Node.js, React, TypeScript.
- Understanding of MLOps and deployment practices.
- Strong analytical, problem-solving, and communication skills.
- 4+ years of experience with GenAI/LLMs in production.
- Experience with agent orchestration frameworks (CrewAI, LangGraph, Autogen).
- Exposure to client-facing AI solutions or cross-functional projects.
- Open-source contributions, research, or AI project portfolio.
- Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain).
- Hands-on experience with LLMs, RAG, embedding, and prompt tuning.
- Experience building AI agents and multi-agent systems.
- Experience with cloud platforms (AWS/Azure/GCP) and containerization.
- Strong knowledge of REST APIs and data integration.
- Experience with FastAPI, Node.js, React, TypeScript.
- Understanding of MLOps and deployment practices.
- Strong analytical, problem-solving, and communication skills.
- Competitive salary and performance-based bonuses.
- Comprehensive insurance plans.
- Collaborative and supportive work environment.
- Chance to learn and grow with a talented team.
- A positive and fun work environment.
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Section · 02
Skills
RAG embeddingDockerMLOpsgoogle cloud platformamazon web servicesLarge Language Models (LLMs)Multi-Agent SystemsPrompt tuningPythonNode.JsTypescriptReact.JsSQLREST APIData IntegrationMicrosoft AzureAgentic AiFastAPI