We are looking for a highly skilled Ai/ML Engineer with a strong fullstack Python background to design, develop, and integrate intelligent systems focused on Gen Ai, Agentic AI, large language models (LLMs), RAG, prompt engineering, and advanced context management. In this role, you will play a critical part in architecting context-rich AI solutions, crafting effective prompts, and ensuring seamless agent interactions using frameworks like LangGraph.
Key Responsibilities:
Prompt & Context Engineering:
Design, optimize, and evaluate prompts for LLMs to achieve precise, reliable, and contextually relevant outputs across a variety of use cases.
Context Management:
Architect and implement dynamic context management strategies, including session memory, retrieval-augmented generation, and user personalization, to enhance agent performance.
LLM Integration:
Integrate, fine-tune, and orchestrate LLMs within Python-based applications, leveraging APIs and custom pipelines for scalable deployment.
LangGraph & Agent Flows:
Build and manage complex conversational and agent workflows using the LangGraph framework to support multi-agent or multi-step solutions.
Fullstack Development:
Develop robust backend services, APIs, and (optionally) front-end interfaces to enable end-to-end AI-powered applications.
Collaboration:
Work closely with product, data science, and engineering teams to define requirements, run prompt experiments, and iterate quickly on solutions.
Evaluation & Optimization:
Implement testing, monitoring, and evaluation pipelines to continuously improve prompt effectiveness and context handling.
Required Skills & Qualifications:
Deep experience with fullstack Python development (FastAPI, Flask, Django; SQL/NoSQL databases).
Demonstrated expertise in prompt engineering for LLMs (e.g., OpenAI, Anthropic, open-source LLMs).
Proven implementation of
Retrieval-Augmented Generation (RAG)
pipelines
Experience of software engineering, machine/deep learning engineering, or applied AI experience
Experience with cloud platforms such as AWS, GCP, or Azure
Experience with Docker, Kubernetes, CI/CD, and production deployment practices
Deploy scalable AI services to cloud environments using modern software engineering and MLOps practices
Strong understanding of context engineering, including session management, vector search, and knowledge retrieval strategies.
Hands-on experience integrating AI agents and LLMs into production systems.
Proficient with conversational flow frameworks such as LangGraph.
Familiarity with cloud infrastructure, containerization (Docker), and CI/CD practices.
Exceptional analytical, problem-solving, and communication skills.
Preferred:
Experience evaluating and fine-tuning LLMs or working with RAG architectures.
Background in information retrieval, search, or knowledge management systems.
Contributions to open-source LLM, agent, or prompt engineering projects.