Software Engineer - AI Agents

A typical day as a Cogniter will contain the following tasks & responsibilities

• As a Solutions Engineer, you will collaborate with solutions architects and global delivery teams to develop solutions and applications on Cognite products and platform 
• Integrations are well thought out and robust Important quality criteria for the solution are met (E.g. CI/CD, logging, security)
• Develop technology components in alignment with the  overall technical solution and ensure technical fit within the customer ecosystem and target architecture
• Design integration and data model using Cognite data connectors, Cognite platform components,  SQL, Python/Java and Rest APIs
• Design, develop, and implement generative AI solutions with a strong focus on AI agents, multi-agent systems, and the latest generative AI technologies to drive business innovation and enhance customer experiences.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical specifications for generative AI solutions.
• In collaboration with Solutions architects, develop scalable AI solutions, including AI agents, that integrate seamlessly with existing systems and leverage cutting-edge technologies.
• Develop and deploy AI agents capable of autonomous task execution, environment adaptation, and effective interaction with users and systems, utilizing the latest generative AI frameworks and models.
• Vector Database Proficiency: Knowledge of vector databases like Pinecone, Milvus, Weaviate, or Faiss, including their architecture and use cases.
• Vector Embedding Creation: Experience in generating vector embeddings from textual, visual, or other data using common industry models.
• Skills in creating, managing, and optimizing indexes for efficient similarity search within vector databases, including knowledge of ANN search algorithms.
• Data Ingestion and Querying: Proficiency in ingesting large datasets into vector databases and writing optimized queries for complex similarity searches.
• Scaling and Performance Tuning: Ability to scale vector databases to handle large datasets and optimize search performance through resource management and index tuning.
• Document Retrieval and Prompt Engineering: Skills in designing effective document retrieval strategies and crafting prompts that leverage retrieved documents in the generation process.
• Data Pipeline and Deployment: Expertise in managing data pipelines for RAG systems, from ingestion to retrieval and generation, and deploying RAG systems at scale.

We believe most of these should match your experience

• 2 to 5+ years of experience in software engineering, with a focus of at least 2+ years in AI and 1+ years on Generative AI, machine learning, or intelligent systems.
• Proven experience in developing and deploying multi-agent systems, preferably using frameworks like LangChain. (Mandatory experience) 
• Experience with knowledge graphs, graph databases, or related technologies.
• RAG Architecture Understanding: In-depth knowledge of Retrieval-Augmented Generation (RAG) systems, integrating retrieval with generative models to produce informed responses.
• Model Integration and Fine-Tuning: Experience in integrating and fine-tuning pre-trained models with retrieval systems in RAG pipelines for enhanced performance
• Proficiency in Python, JavaScript, or other relevant programming languages.
• Deep understanding of multi-agent frameworks, including agent communication, decision-making, and learning strategies.
• Familiarity with cloud platforms (e.g., AWS, Azure) and containerization technologies (e.g., Docker, Kubernetes).
• Experience with API development and integration.
• Strong problem-solving skills and the ability to think critically about complex systems.
• Excellent communication skills, with the ability to explain technical concepts to both technical and non-technical stakeholders.
• Ability to work in a fast-paced, collaborative environment and manage multiple priorities.
• Experience in the industrial sector or with industrial data. (Not mandatory) 
• Knowledge of big data technologies (e.g., Hadoop, Spark) and real-time processing frameworks.
• Data Handling and Storage: Proficiency in reading and writing data in various formats (CSV, JSON, SQL) and using storage tools like SQLite and SQL databases.
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