• Design, develop, and productionize advanced agentic systems that: • Handle large volumes of security event data • Operate with resilience in noisy, real-world environments • Continuously evolve as new attack patterns and tools emerge • Improve and harden existing agentic systems • Work with LLMs in production environments, not just prototypes • Engineer robust context pipelines for large-scale data ingestion and reasoning • Optimize systems for performance, reliability, and failure handling • Evaluate and integrate new agent frameworks, tools, and approaches • Rapidly hypothesize -> build -> test -> validate -> iterate

Role Description We’re seeking a Python Developer to join our innovative AI team. This role is focused on building intelligent agents using Microsoft Autogen (or similar tools) and integrating them into AI-driven platforms. You’ll work closely with architects, data engineers, and other developers to create dynamic, adaptive systems that interact with users, data, and tools autonomously. Your work will power solutions in various domains including automation, analytics, and LLM-based product features. Responsibilities - Design and develop intelligent agents using Python and Autogen framework (or similar frameworks) - Collaborate with AI/ML teams to integrate LLMs (e.g., OpenAI, Anthropic, Google, etc.) into multi-agent workflows - Develop and maintain cloud-native applications and services (Azure / AWS / GCP) - Create robust APIs and integrate them with external/internal systems - Implement data ingestion, transformation, and storage logic using SQL, NoSQL, Columnar and Vector DBs - Support system scalability, performance, and maintainability using clean architecture principles - Contribute to agent orchestration logic, tool integrations, and interaction flows - Work closely with the Solutions Architect to align implementation with architecture vision - Write tests, maintain documentation, and participate in code reviews Qualifications - 4+ years of experience in Python development, including async programming - Hands-on experience with LLM agent frameworks, especially Microsoft Autogen, Langchain, Crew AI, LlamaIndex, etc. - Solid knowledge of cloud platforms: Azure, AWS, or GCP (at least one required) - Strong understanding of web development and modern API design (e.g., FastAPI, Flask) - Good experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, Redis) - Understanding of Machine Learning basics (e.g., inference pipelines, embeddings, vector stores) - Familiarity with containerized development (Docker) and cloud deployment practices - Solid Git workflow experience, CI/CD pipelines knowledge - English proficiency (Intermediate or higher) Nice to Have - Experience building multi-agent systems or working with agent orchestration tools - Exposure to RAG (Retrieval-Augmented Generation) and vector search (e.g., FAISS, Weaviate) - Familiarity with LangChain, LangGraph, LlamaIndex, CrewAI - Hands-on experience with streaming data, event-driven architecture, or message queues (e.g., Kafka, RabbitMQ and its cloud versions like Kinesis and MQ) - Knowledge of observability tools (e.g., Prometheus, Grafana, OpenTelemetry) - Experience with DevOps tools and infrastructure-as-code (Terraform, Helm, etc.)

• Design, develop, and deploy machine learning and AI models (e.g., NLP, recommendation engines, personalization algorithms) • Integrate AI capabilities into web applications via APIs, microservices, and cloud platforms • Optimize models for performance, scalability, and responsiveness in web environments • Conduct data preprocessing, model training, testing, and validation for production deployment • Collaborate with UI/UX designers to embed AI-driven features into website interfaces and workflows • Implement intelligent features such as: - Personalized content and recommendations - AI-powered chatbots and conversational interfaces - Dynamic UI/UX adjustments based on user behavior • Support automated design processes (e.g., layout optimization, A/B testing, content generation) • Develop and integrate AI-enhanced frontend components using modern frameworks (e.g., React, Vue) • Ensure responsive, accessible, and high-performance user interfaces • Work with frontend teams to translate design concepts into intelligent, interactive web features • Optimize applications for speed, scalability, and cross-browser compatibility • Analyze user behavior and web interaction data to improve design and functionality • Build predictive models to enhance UX (e.g., personalization, search optimization, recommendations) • Generate insights that inform design improvements and user engagement strategies • Collaborate with product managers, designers, and engineers to define AI-driven requirements • Participate in system architecture, design reviews, and agile development processes • Monitor deployed AI systems and continuously improve performance and accuracy • Stay current with emerging AI, UX, and web technology trends

• Design, develop, and deploy AI solutions leveraging LLMs, RAG, and agentic AI architectures • Design, evaluate, and maintain a seamless generative pipeline that accepts user queries/prompts in conversational language, handles real-time prompt orchestration to generate safe SQL, extracts required insights, and reformats raw datasets into clean, actionable, human-readable answers • Evaluate business processes and identify opportunities where generative AI and autonomous agents can improve efficiency, decision-making, and customer outcomes • Architect multi-agent and agentic workflows using modern frameworks • Develop AI systems that effectively utilize tools, APIs, enterprise data sources, and external services to perform complex tasks • Implement prompt engineering, model evaluation, guardrails, observability, and governance practices for production AI applications • Build and optimize RAG pipelines, vector databases, knowledge retrieval systems, and semantic search capabilities • Assess emerging AI models and technologies to determine the most effective and cost-efficient solutions for client needs • Integrate AI capabilities into enterprise software ecosystems, cloud platforms, and business workflows • Design and implement AI monitoring, testing, evaluation, and continuous improvement processes • Collaborate with business stakeholders, solution architects, software engineers, and data scientists to deliver AI-driven solutions • Present AI concepts, recommendations, architectures, and implementation strategies to technical and executive audiences