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

• As a Solutions Architect, you will be an expert in how we implement our Cognite Saa S offerings and applications and ensuring that the data is efficiently modeled and contextualized in Cognite Data Fusion (E.g graph and relational)
• Integrations are well thought out and robust Important quality criteria for the solution are met (E.g. CI/CD, logging, security)
• Operational responsibility and support agreements for the delivered solution are clearly defined
• Design 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
• Advisor to our customers and partners on Cognite Data Fusion, Cognite Atlas AI and Cognite products, solutions, and platforms
• 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.
• Architect scalable AI solutions, including AI agents, that integrate seamlessly with existing systems and leverage cutting-edge technologies.
• Drive development of AI frameworks, and tools, with a particular emphasis on generative AI, AI agents, natural language processing (NLP), agent orchestration, and reinforcement learning.
• Provide technical leadership and mentorship to software engineers, with a focus on developing skills in generative AI, AI agents, and advanced AI technologies.
• Stay ahead of the latest trends and advancements in AI, particularly in generative AI, AI agents, and associated technologies, to ensure our solutions remain cutting-edge and industry-leading.
• 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.
• Collaborate with Global Delivery on customer engagements, to demonstrate the value and feasibility of AI-driven solutions, particularly those involving AI agents, generative AI technologies, and advanced AI methodologies.
• 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.
• Cross-Skills in Embedding and Security: Proficiency in generating and transforming embeddings for both retrieval and generation tasks, with a focus on security, compliance, and API integration

We believe most of these should match your experience

• 10+ years of experience in software engineering, with a focus of at least 5+ years in AI and 2+ years on Generative AI, machine learning, or intelligent systems.
• Proven experience in developing and deploying multi-agent systems, preferably using frameworks like Lang Chain. (Mandatory experience)
• Experience with knowledge graphs, graph databases, or related technologies (e.g., Neo4j, RDF, SPARQL).
• 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, Java Script, or other relevant programming languages.
• Deep understanding of multi-agent frameworks, including agent communication, decision-making, and learning strategies.
• Strong background in knowledge of graph technologies and their applications.
• Experience in Time Series Analysis involving detecting trends and patterns, forecasting future values using models like ARIMA, SARIMA, Prophet, and LSTMs, and identifying anomalies or outliers in time series data.
• Experience in optimizing the performance of AI agents in real-time, high-load environments, including the use of reinforcement learning and other adaptive techniques.
• 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.
• Feature Engineering and Dimensionality Reduction: Expertise in creating new features to enhance model performance and using techniques like PCA and t-SNE to reduce feature dimensionality while retaining key information.
• Expertise with Model Interpretability by using Tools and frameworks like SHAP (SHapley Additive ex Planations), LIME (Local Interpretable Model-agnostic Explanations), and feature importance to understand model predictions.

• Join an organization of 70 different nationalities \u D83C\u DF10 with Diversity, Equality and Inclusion (DEI) in focus \u D83E\u DD1D
• ITPL office location (Bengaluru)
• A highly modern and fun working environment with sublime culture across the organization, follow us on Instagram @cognitedata \u D83D\u DCF7 to know more
• Flat structure with direct access to decision-makers, with minimal amount of bureaucracy
• Opportunity to work with and learn from some of the best people on some of the most ambitious projects found anywhere, across industries
• Join our HUB \u D83D\u DDE3️ to be part of the conversation directly with Cogniters and our partners.
• Hybrid work environment globally