Role OverviewThis is a strategic and hands-on role at the intersection of LLMs, agent-based systems, and enterprise AI engineering. You’ll design and implement autonomous agent workflows, enrich LLMs with structured knowledge, and architect tool-using agents capable of solving real-world business problems. Key ResponsibilitiesDesign, fine-tune, and evaluate Large Language Models (LLMs) for agent-based reasoning, natural language interaction, and workflow orchestration.Build autonomous agents using frameworks like LangChain, AutoGen, CrewAI, or similar, integrated with tool use, memory, and multi-step planning.Implement Agentic Frameworks and Digital TwinsDevelop and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., FAISS, Weaviate) and hybrid architectures like GraphRAG.Engineer agent workflows with tool calling, multi-agent collaboration, and long-term memory (episodic + semantic).Monitor and improve performance metrics like reasoning accuracy, hallucination rate, and latency under production constraints.Collaborate with DevOps, product, and platform teams to scale agents using Docker, Kubernetes, and Azure ML. Must-Have Skills3–6 years of experience in LLM-based NLP / AI roles, with hands-on deployment experience.Proficiency in LangChain, AutoGen, CrewAI, or similar agent frameworks (real-world examples preferred).Strong understanding of agent memory, task planning, and function/tool calling workflows.Experience with RAG pipelines, embedding models, and vector search systems.Deep knowledge of Transformers, Hugging Face ecosystem, and custom prompt engineering.Proficiency in Python and ML libraries like PyTorch, TensorFlow.Familiarity with knowledge graphs, enterprise data systems, and orchestration strategies. Preferred QualificationsExperience designing persona-aware agents for enterprise use cases (BFSI, healthcare, public sector).Familiarity with BertGraph, GraphRAG, or similar graph-augmented architectures.Experience with Azure AI Studio, OpenAI APIs, Anthropic Claude, or Meta LLaMA models.Background in agent evaluation: using metrics for tool use accuracy, conversation consistency, and action success rates.
Role Overview As a Full Stack Engineer at Stralto Global, you’ll be responsible for developing and maintaining robust web applications, ensuring real-time interactivity, performance, and seamless integration with data and AI services. You'll collaborate with cross-functional teams including AI engineers, product strategists, and UX designers. Key Responsibilities Develop responsive and dynamic frontend applications using Angular (latest version). Implement real-time features using WebSockets for streaming data and agent communication. Build and maintain backend APIs and services using Python and Flask , ensuring performance and scalability. Design and query relational databases (PostgreSQL/MySQL) efficiently; write optimized SQL queries. Integrate front-end components with backend services, ensuring end-to-end reliability and performance. Collaborate closely with DevOps, product, and AI teams to deploy features into production. Write clean, maintainable, and well-documented code. Conduct unit and integration testing to ensure robustness and reliability. Participate in architecture discussions and technical reviews to guide feature development. Required Skills 3–6 years of experience building full stack applications in production environments. Strong proficiency in Angular (components, services, routing, RxJS). Experience with WebSocket-based communication , preferably in enterprise or high-throughput environments. Proficiency in Python and backend development using Flask . Solid experience with SQL databases , query optimization, and schema design. Familiarity with RESTful APIs and application state management. Understanding of deployment best practices, version control (Git), and agile development workflows. Nice-to-Have Experience with Azure , Docker , or Kubernetes for deploying applications at scale. Background in real-time dashboards , data visualizations , or event-driven systems . Knowledge of authentication protocols (OAuth2, JWT) and API security. Exposure to AI/ML-based platforms or previous experience working with data scientists or ML engineers. Show more Show less
Role Overview This is a strategic and hands-on role at the intersection of LLMs , agent-based systems , and enterprise AI engineering . You’ll design and implement autonomous agent workflows , enrich LLMs with structured knowledge, and architect tool-using agents capable of solving real-world business problems. Key Responsibilities Design, fine-tune, and evaluate Large Language Models (LLMs) for agent-based reasoning, natural language interaction, and workflow orchestration. Build autonomous agents using frameworks like LangChain , AutoGen , CrewAI , or similar, integrated with tool use, memory, and multi-step planning. Implement Agentic Frameworks and Digital Twins Develop and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases (e.g., FAISS, Weaviate) and hybrid architectures like GraphRAG . Engineer agent workflows with tool calling , multi-agent collaboration , and long-term memory (episodic + semantic) . Monitor and improve performance metrics like reasoning accuracy, hallucination rate, and latency under production constraints. Collaborate with DevOps, product, and platform teams to scale agents using Docker, Kubernetes, and Azure ML. Must-Have Skills 3–6 years of experience in LLM-based NLP / AI roles, with hands-on deployment experience. Proficiency in LangChain , AutoGen , CrewAI , or similar agent frameworks (real-world examples preferred). Strong understanding of agent memory , task planning , and function/tool calling workflows . Experience with RAG pipelines , embedding models , and vector search systems . Deep knowledge of Transformers , Hugging Face ecosystem , and custom prompt engineering. Proficiency in Python and ML libraries like PyTorch, TensorFlow. Familiarity with knowledge graphs , enterprise data systems , and orchestration strategies. Preferred Qualifications Experience designing persona-aware agents for enterprise use cases (BFSI, healthcare, public sector). Familiarity with BertGraph , GraphRAG , or similar graph-augmented architectures. Experience with Azure AI Studio , OpenAI APIs , Anthropic Claude , or Meta LLaMA models. Background in agent evaluation: using metrics for tool use accuracy, conversation consistency, and action success rates.
As a Full Stack Engineer at Stralto Global, your primary responsibility will be the development and maintenance of robust web applications, ensuring real-time interactivity, performance, and seamless integration with data and AI services. You will collaborate with cross-functional teams, including AI engineers, product strategists, and UX designers. You will be responsible for developing responsive and dynamic frontend applications using Angular, implementing real-time features using WebSockets for streaming data and agent communication. Additionally, you will build and maintain backend APIs and services using Python and Flask, ensuring performance and scalability. Your role will involve designing and querying relational databases efficiently, integrating front-end components with backend services, collaborating closely with DevOps, product, and AI teams to deploy features into production, and writing clean, maintainable, and well-documented code. Furthermore, you will conduct unit and integration testing to ensure robustness and reliability, as well as participate in architecture discussions and technical reviews to guide feature development. The ideal candidate will have at least 3-6 years of experience building full stack applications in production environments. You should have strong proficiency in Angular, experience with WebSocket-based communication, proficiency in Python and Flask for backend development, solid experience with SQL databases and query optimization, familiarity with RESTful APIs and application state management, and an understanding of deployment best practices, version control (Git), and agile development workflows. Nice-to-have skills include experience with Azure, Docker, or Kubernetes for deploying applications at scale, a background in real-time dashboards, data visualizations, or event-driven systems, knowledge of authentication protocols such as OAuth2 and JWT, and exposure to AI/ML-based platforms or previous experience working with data scientists or ML engineers.,